Findings & Analysis

3 Exploratory Data Analysis (EDA) on Sailor Shift’s career

We will design and develop visualizations and visual analytic tools that will allow Silas to explore and understand the profile of Sailor Shift’s career.

Note

This analysis examines Sailor Shift’s work during her solo artist career, excluding her initial collaborative period with Ivy Echos.

Future work: What if we make everything into a function and find members that the person is part of and use a while loop to get everything so it includes all groups that the person is a part of?

3.1 Visualising Sailor Shift’s Music

The analysis begins by extracting Sailor Shift’s name from the node dataset. Following this, all outgoing edges originating from her node are identified. The resulting nodes from those edges are filtered to isolate only those classified as Songs and Albums, thereby displaying her musical career.

# Step 0: Get name of 'Sailor Shift'
sailor_vertex_name <- mc1_nodes_clean %>%
  filter(is_sailor == TRUE) %>%
  pull(name)

# Step 1: Find outgoing edges from Sailor Shift
sailor_out_edges <- mc1_edges_clean %>%
  filter(from == sailor_vertex_name)

# Step 2: Identify neighbour node names
sailor_out_node_names <- sailor_out_edges$to

# Step 4: Identify songs/albums
sailor_music_all <- mc1_nodes_clean %>%
  filter(name %in% sailor_out_node_names, `Node Type` %in% c("Song", "Album")) %>%
  pull(name)

# Step 5: Build subgraph using names
sub_nodes <- unique(c(sailor_vertex_name, sailor_music_all))

sub_graph <- graph %>%
  activate(nodes) %>%
  filter(name %in% sub_nodes)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name %in% c("Sailor Shift", "Ivy Echos", "Wei Zhao"), 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == "Sailor Shift", "Sailor Shift",
              ifelse(node_name == "Ivy Echos", "Ivy Echos",
              ifelse(node_name == "Wei Zhao", "Wei Zhao", NA)))
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)

Sailor Shift’s Music Insights

• In total, Sailor Shift released 38 Songs and Albums from 2024 to 2040
• Details on the Songs and Albums can be found in the table below
• Sailor Shift released 4 Songs in 2028
  • Among those, only 1 is a Single
  • Therefore, the viral Song that spark her breakthrough is High Tide Heartbeat
• Among her Songs and Albums, 36 belong to the Oceanus Folk genre, while there is 1 Americana and 1 Synthwave Album each.

mc1_nodes_clean %>%
  filter(name %in% sailor_music_all) %>%
  arrange(release_date) %>%
  select(`Node Type`, node_name, release_date, genre, notable, single, notoriety_date) %>%
  kable() %>%
  kable_styling("striped", full_width = F) %>% 
  scroll_box(height = "300px")

Node Type	node_name	release_date	genre	notable	single	notoriety_date
Album	The Kelp Forest Canticles	2024	Oceanus Folk	TRUE	NA	NA
Album	Luminescent Tides	2025	Oceanus Folk	TRUE	NA	NA
Album	Shoreline Sonnets	2026	Oceanus Folk	TRUE	NA	NA
Album	Tidal Pop Waves	2028	Oceanus Folk	TRUE	NA	NA
Song	Chord of the Deep	2028	Oceanus Folk	FALSE	FALSE	NA
Song	Electric Eel Love	2028	Oceanus Folk	TRUE	FALSE	NA
Song	High Tide Heartbeat	2028	Oceanus Folk	FALSE	TRUE	NA
Song	Sun-Drenched Daydream	2028	Oceanus Folk	FALSE	FALSE	NA
Album	Tides of Echos	2029	Oceanus Folk	TRUE	NA	NA
Album	Salty Dreams	2030	Oceanus Folk	TRUE	NA	NA
Song	Driftwood Lullaby	2030	Oceanus Folk	FALSE	FALSE	NA
Song	Heart of the Habitat	2030	Oceanus Folk	FALSE	TRUE	NA
Song	Reef Rhythm	2030	Oceanus Folk	FALSE	FALSE	NA
Song	Seashell Serenade	2030	Oceanus Folk	TRUE	TRUE	2030
Album	Hidden Depths	2031	Oceanus Folk	TRUE	NA	NA
Album	Neon Heartbeat	2031	Synthwave	FALSE	NA	NA
Album	Submerged Sonatas	2031	Oceanus Folk	TRUE	NA	NA
Album	Tidesworn Ballads	2031	Oceanus Folk	TRUE	NA	NA
Album	The Current & The Chord	2032	Oceanus Folk	TRUE	NA	NA
Song	Saltwater Hymn	2032	Oceanus Folk	FALSE	FALSE	NA
Album	Ballads for the End of Time	2033	Oceanus Folk	TRUE	NA	NA
Album	Melancholy Circuitry	2033	Americana	TRUE	NA	NA
Album	Coral Beats	2034	Oceanus Folk	TRUE	NA	NA
Album	Drifting Between the Stars and the Sea	2034	Oceanus Folk	TRUE	NA	NA
Song	Barnacle Heart	2034	Oceanus Folk	FALSE	FALSE	NA
Song	Into the Current	2034	Oceanus Folk	FALSE	TRUE	NA
Song	Moon Over the Tide	2034	Oceanus Folk	TRUE	FALSE	NA
Album	Artificial Sunsets	2035	Oceanus Folk	TRUE	NA	NA
Album	Tides & Ballads	2036	Oceanus Folk	TRUE	NA	NA
Song	Fog & Fiddle	2036	Oceanus Folk	TRUE	FALSE	NA
Song	The Fisherman's Prayer	2036	Oceanus Folk	FALSE	TRUE	NA
Album	Ballads for the Low Tide	2037	Oceanus Folk	TRUE	NA	NA
Album	Electric Reverie	2038	Oceanus Folk	TRUE	NA	NA
Album	Oceanbound	2038	Oceanus Folk	TRUE	NA	NA
Song	Stormsong	2038	Oceanus Folk	TRUE	TRUE	NA
Album	Echoes of the Deep	2040	Oceanus Folk	TRUE	NA	NA
Song	Salt in My Veins	2040	Oceanus Folk	FALSE	FALSE	NA
Song	The Last Mariner	2040	Oceanus Folk	FALSE	FALSE	NA

4.2 Question 1a) Who has she been most influenced by over time?

This section is a continuation from the previous section where all her outgoing edges were found. Music that she produced were already found and now Persons and Musical Groups that are outgoing from her are included. From these Songs, Albums, Persons and Musical Groups, only those Songs and Albums with further outgoing edges are kept. Then, the nodes connected to those are found. These are music that her music have been influenced by. Finally, the last step finds Person and Musical Groups who produced the music that have influenced her music.

Note: The term music refers to either Song or Album.

# Data Preparation

# Step 1: Get all creator names
global_creators <- mc1_nodes_clean %>%
  filter(`Node Type` %in% c("Person", "MusicalGroup"))

# Step 2: Get all outgoing edges from these creators
creator_out_edges <- mc1_edges_clean %>%
  filter(from %in% global_creators$name, `Edge Colour` == "Creator Of")

# Step 3: Get all songs made by creators
creator_music <- mc1_nodes_clean %>%
  filter(name %in% creator_out_edges$to)

# Step 4: Get all outgoing edges from songs
creator_songs_out_edges <- mc1_edges_clean %>%
  filter(from %in% creator_music$name, `Edge Colour` == "Influenced By")

# Step 5: First join to get creator names (from)
creators <- creator_out_edges %>%
  left_join(mc1_nodes_clean, by = c("from" = "name")) %>%
  select(from, to, node_name, `Node Type`) %>%
  rename(creator_from = from, creator_name = node_name, creator_node_type = `Node Type`)

# Step 6: Second join to get song names (to)
creator_and_songs <- creators %>%
  left_join(mc1_nodes_clean, by = c("to" = "name")) %>%
  select(creator_from, creator_name, creator_node_type, to, node_name, release_date, genre, notable) %>%
  rename(song_name = node_name, song_genre = genre, song_to = to) %>%
  distinct()

# Step 7: Third join to get song's influenced genre (to)
creator_and_songs_and_influenced_by <- creator_and_songs %>%
  left_join(creator_songs_out_edges %>% select(from, to), by = c("song_to" = "from"), relationship = "many-to-many") %>%
  left_join(mc1_nodes_clean %>% select(name, genre), by = c("to" = "name")) %>%
  rename(influenced_by_genre = genre, influenced_by = to) %>%
  distinct()

# Step 8: Fourth join to get influenced song's creator
creator_and_songs_and_influenced_by_creator <- creator_and_songs_and_influenced_by %>%
  left_join(creator_out_edges %>% select(from, to), by = c("influenced_by" = "to"), relationship = "many-to-many") %>%
  rename(influenced_by_creator = from)

# Data Preparation

# Step 1: Get the node of the sailor
sailor_node <- creator_and_songs_and_influenced_by_creator %>%
  filter(creator_name == "Sailor Shift") %>%
  pull(creator_from) %>%
  unique()

# Step 2: Get the songs that the creator produced
sailor_songs <- creator_and_songs_and_influenced_by_creator %>%
  filter(creator_from == sailor_node,
         creator_from != influenced_by) %>%
  pull(song_to)

# Step 3: Get the songs they have influenced by
sailor_songs_influenced <- creator_and_songs_and_influenced_by_creator %>%
  filter(creator_from == sailor_node,
         creator_from != influenced_by) %>%
  pull(influenced_by)

# Step 5: Get the creators of the influenced by songs
sailor_songs_influenced_creators <- creator_and_songs_and_influenced_by_creator %>%
  filter(creator_from == sailor_node,
         creator_from != influenced_by) %>%
  pull(influenced_by_creator)

# Step 5: Combine all relevant node names
all_node_names <- unique(c(
  sailor_node,
  sailor_songs,
  sailor_songs_influenced,
  sailor_songs_influenced_creators
))

# Step 6: Filter graph to relevant nodes only
sub_graph <- graph %>%
  filter(name %in% all_node_names)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name %in% c("Sailor Shift", "Wei Zhao"), 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == "Sailor Shift", "Sailor Shift",
              ifelse(node_name == "Wei Zhao", "Wei Zhao", NA))
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)

Who has Sailor Shift been most influenced by over time?

• The table below shows all the Person and Musical Group that have influenced Sailor Shift
• Through the graph, most of the other Person and Musical Group have only produced a single music that has influenced one of her music.
• However, Wei Zhao stands out because they have influenced Sailor Shift more than once.
  • Wei Zhao is the producer of Susurros de Passion that has influenced the her album Salty Dreams. Wei Zhao is also the composer of Silent Steps in the Forest’s Embrance that has influenced her song Moon Over the Tide.
• In conclusion, Ivy Echos and Wei Zhao have been the most influential on Sailor Shift’s music career.

# Extract node data
node_data <- as_tibble(sub_graph)

# View all Person and Musical Group that has influenced Sailor Shift
node_data %>%
  filter(name != "Sailor Shift", `Node Type` %in% c("Person", "MusicalGroup")) %>%
  select(`Node Type`, node_name) %>%
  kable() %>%
  kable_styling("striped", full_width = F) %>% 
  scroll_box(height = "200px")

Node Type	node_name
MusicalGroup	Phantom Roots
MusicalGroup	Ursus
Person	Amico Luciani
Person	Amy Powell
Person	Belinda Knappe
Person	Brooke Olson
Person	Caitlin Miller
Person	Cipriano Peranda
Person	Daniel Mccormick
Person	David Reese
Person	Debra Graham
Person	Dylan Schwartz
Person	Edward Evans
Person	Elmo Calbo
Person	Fang Ding
Person	Gang Shao
Person	Guiying Ren
Person	Herbert Mcfarland
Person	Igor Dyś
Person	James Henderson
Person	James Medina
Person	Jason Aguirre
Person	Jeremiah Love
Person	Jing Kang
Person	Joanna Avery
Person	Joel Long
Person	John Graves
Person	Joseph Ponce
Person	Joshua Taylor
Person	Juan Shi
Person	Juan Zhou
Person	Judy Yang
Person	Jun Qiao
Person	Jun Zhou
Person	Kaori Ito
Person	Kimberly Estrada
Person	Lei Fu
Person	Lei Jin
Person	Lei Shi
Person	Linda Burns
Person	Lori Massey
Person	Matthew Best
Person	Matthew Lane
Person	Michael Nixon
Person	Min Cao
Person	Min Long
Person	Ming Huang
Person	Ming Long
Person	Ming Qiao
Person	Ming Ren
Person	Ming Zhou
Person	Miss Jennifer Williams
Person	Molly Gonzalez
Person	Na Ye
Person	Naoko Sato
Person	Nathan Jones
Person	Ping Jin
Person	Ping Liao
Person	Qiang Liu
Person	Qiang Yuan
Person	Qiang Zhou
Person	Rachel Montes
Person	Rebecca Wise
Person	Robert Woods
Person	Ryan Dunlap
Person	Sailor Shift
Person	Samantha Bullock
Person	Sandra Burke
Person	Sean Jones
Person	Shannon Harvey
Person	Shawn Johnson
Person	Sheryl Roman
Person	Simone Säuberlich
Person	Tao Gao
Person	Tao Hu
Person	Tao Wen
Person	Trevor Bass
Person	Urszula Stochmal
Person	Wei Liang
Person	Wei Zhao
Person	William Robinson
Person	Xia Yuan
Person	Xiulan Fang
Person	Xiuying Du
Person	Yan Dai
Person	Yan Li
Person	Yan Tang
Person	Yang Peng
Person	Yang Wan
Person	Yang Zhao
Person	Yoko Hashimoto
Person	Yong Dong
Person	Yong Lu
Person	Zachary Francis

4.3 Question 1b) Who has she collaborated with and directly or indirectly influenced?

The analysis begins with the sailor_music_all dataset from Section 4.1, filtering to retain only music with more than two inward edges. This step eliminates works without collaborators or external influences. Two Song/Albums (“The Current & The Chord” and “Salty Dreams”) have to be manually removed since their inward edges are from Sailor herself and therefore not needed. Next, all inward neighbours (source nodes) of Sailor’s music are identified, representing both influenced works (Songs/Albums) and collaborators (Persons/Musical Groups). After filtering to keep only Songs and Albums, their inward neighbours are examined. This reveals: (1) Persons/Musical Groups who have been influenced by Sailor’s music (Persons/Musical Groups), and (2) secondary influenced works (Songs/Albums) that were impacted by the same sources that were influenced by Sailor’s compositions.

# Data Preparation

# Step 1: Get all creator names
global_creators <- mc1_nodes_clean %>%
  filter(`Node Type` %in% c("Person", "MusicalGroup"))

# Step 2: Get all outgoing edges from these creators
creator_out_edges <- mc1_edges_clean %>%
  filter(from %in% global_creators$name, `Edge Colour` == "Creator Of")

# Step 3: Get all songs made by creators
creator_music <- mc1_nodes_clean %>%
  filter(name %in% creator_out_edges$to)

# Step 4: Get all incoming edges from songs (music and collaborators)
creator_songs_in_edges <- mc1_edges_clean %>%
  filter(to %in% creator_music$name)

# Step 5: First join to get creator names (from)
creators <- creator_out_edges %>%
  left_join(mc1_nodes_clean, by = c("from" = "name")) %>%
  select(from, to, node_name, `Node Type`) %>%
  rename(creator_name = node_name, creator_node_type = `Node Type`)

# Step 6: Second join to get song names (to)
creator_and_songs <- creators %>%
  left_join(mc1_nodes_clean, by = c("to" = "name")) %>%
  select(from, creator_name, creator_node_type, to, node_name, release_date, genre, notable) %>%
  rename(creator_from = from, song_name = node_name, creator_release_date = release_date, song_genre = genre, song_to = to) %>%
  distinct()

# Step 7: Third join to get influenced songs / collaborators
creator_and_songs_and_influences <- creator_and_songs %>%
  left_join(creator_songs_in_edges %>% select(from, to, `Edge Colour`), by = c("song_to" = "to"), relationship = "many-to-many") %>%
  left_join(mc1_nodes_clean %>% select(name, genre, node_name, release_date), by = c("from" = "name")) %>%
  rename(influence_genre = genre, infuence_music_collaborate = from, infuence_music_collaborate_name = node_name, influence_release_date = release_date) %>%
  distinct()

# Step 8: Fourth join to get influenced song's creator
creator_and_songs_and_influences_and_creators <- creator_and_songs_and_influences %>%
  left_join(creator_out_edges %>% select(from, to), by = c("infuence_music_collaborate" = "to"), relationship = "many-to-many") %>%
  rename(influence_creator = from)

# Step 9: Fifth join to get influenced song's creator name
creator_and_songs_and_influences_and_creators <- creator_and_songs_and_influences_and_creators %>%
  left_join(mc1_nodes_clean %>% select(name, node_name), by = c("influence_creator" = "name")) %>%
  rename(influence_creator_name = node_name)

# Step 10: Add release date for collaborators
creator_and_songs_and_influences_and_creators_collaborate <- creator_and_songs_and_influences_and_creators %>%
  mutate(
    influence_release_date = case_when(
      `Edge Colour` == "Creator Of" ~ creator_release_date,
      TRUE ~ influence_release_date  # Keeps original value if not "Creator Of"
    ),
    influence_creator = case_when(   # A collaborator can also be considered influenced
      `Edge Colour` == "Creator Of" ~ infuence_music_collaborate,
      TRUE ~ influence_creator
    )
  )

# Data Preparation

# Step 2: Get the songs that the sailor produced
sailor_songs <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from == sailor_node,
         infuence_music_collaborate != sailor_node) %>%
  pull(song_to)

# Step 3: Get the songs they have influenced / artists they collaborate with
sailor_songs_collaborate_influence <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from == sailor_node,
         infuence_music_collaborate != sailor_node) %>%
  pull(infuence_music_collaborate)

# Step 4: Get the songs they have influenced
sailor_songs_influence <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from == sailor_node,
         infuence_music_collaborate != sailor_node,
         `Edge Colour` == "Influenced By") %>%
  pull(infuence_music_collaborate)

# Step 5: Get the influenced creators of the influenced songs
sailor_songs_influence_creators <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from == sailor_node,
         influence_creator != sailor_node,
         !is.na(influence_creator),
         infuence_music_collaborate %in% sailor_songs_influence) %>%
  pull(influence_creator)

all_nodes <- unique(c(sailor_node, 
                      sailor_songs, 
                      sailor_songs_collaborate_influence,
                      sailor_songs_influence_creators))

# Create subgraph
sub_graph <- graph %>%
  filter(name %in% all_nodes)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name == "Sailor Shift", 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == "Sailor Shift", "Sailor Shift", NA)
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)

Who has she collaborated with and directly or indirectly influenced?

• The graph and table shows everyone that she has collaborated with.
  • Sailor Shift has collaborated with 48 different artists and information on them can be found in the table below.
• However, Sailor Shift has not directly or indirectly influenced anyone, since none of her music has influenced others.
  • For example, no Songs/Albums has referenced her Songs or Albums.

collaboration_count <- mc1_nodes_clean %>%
  filter(name %in% all_nodes, 
         `Node Type` %in% c("Person", "MusicalGroup"),
         name != sailor_node) %>%
  distinct(name) %>%
  nrow()

# Print the formatted message
cat(paste0("Sailor Shift has collaborated with ", collaboration_count, 
           " different Persons and Musical Groups\n"))

Sailor Shift has collaborated with 48 different Persons and Musical Groups

mc1_nodes_clean %>%
  filter(name %in% all_nodes, 
         `Node Type` %in% c("Person", "MusicalGroup"),
         name != sailor_node) %>%
  arrange(release_date) %>%
  select(`Node Type`, node_name) %>%
  kable() %>%
  scroll_box(height = "200px")

Node Type	node_name
MusicalGroup	Crimson Carriage
MusicalGroup	Ivy Echos
MusicalGroup	Selkie's Hollow
MusicalGroup	Siren's Call
MusicalGroup	The Brine Choir
MusicalGroup	The Wave Riders
MusicalGroup	Tidal Reverie
MusicalGroup	Violet Engines
Person	Aiden Harper
Person	Arlo Sterling
Person	Astrid Nørgaard
Person	Beatrice Albright
Person	Cassian Rae
Person	Coralia Bellweather
Person	Daniel O'Connell
Person	Elara May
Person	Ethan Clarke
Person	Ewan MacCrae
Person	Finn McGraw
Person	Finn Morgan
Person	Fiona Mercer
Person	Freya Lindholm
Person	Iris Moon
Person	Jade Thompson
Person	Jonah Calloway
Person	Kai Reynolds
Person	Kara Lee
Person	Levi Holloway
Person	Lia Grant
Person	Liam O'Sullivan
Person	Lila "Lilly" Hartman
Person	Lila Rivers
Person	Lucas Bennett
Person	Lyra Blaze
Person	Marin Thorne
Person	Maya Jensen
Person	Maya Torres
Person	Mia Waters
Person	Michael Harris
Person	Olivia Carter
Person	Orion Cruz
Person	Rusty Riggins
Person	Skylar Brooks
Person	Sophie Bennett
Person	Sophie Ramirez
Person	William Tidewell
Person	Zachary Cole
Person	Zane Cruz

4.4 Question 1c) How has she influenced collaborators of the broader Oceanus Folk community?

This question is tackled by first identifying all Songs and Albums belonging to the Oceanus Folk Genre and retriving their associated artists (Persons/Musical Groups). Then Sailor Shift’s is identified and her degree of separation is calculated to examine her impact on the broader Oceanus Folk community.

genre_creators = creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(song_genre == "Oceanus Folk") %>%
  pull(creator_from)

genre_music = creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(song_genre == "Oceanus Folk") %>%
  pull(song_to)

genre_all_nodes <- unique(c(genre_creators,
                    genre_music))

# Create subgraph
sub_graph <- graph %>%
  filter(name %in% genre_all_nodes)


chosen_degree = 13

# Data Preparation

# Convert to igraph object
sub_igraph <- as.igraph(sub_graph)

# Find Sailor Shift's vertex ID
artist_id <- which(V(sub_igraph)$name == sailor_node)

# Calculate distances from Sailor Shift
distances <- distances(sub_igraph, v = artist_id, mode = "all")

# Convert to tidy format, calculate distances and handle infinite values
distance_df <- tibble(
  name = V(sub_igraph)$name,
  degree = as.numeric(distances[1, ])
) %>%
  mutate(degree = ifelse(is.infinite(degree), NA, degree))

filtered_nodes <- distance_df %>%
  filter(degree <= chosen_degree) %>%
  pull(name)

filtered_nodes <- unique(c(filtered_nodes,
                    sailor_node))

# Create subgraph
filtered_graph <- sub_graph %>%
  filter(name %in% filtered_nodes) %>%
  activate(nodes) %>%
  left_join(distance_df, by = "name")

# Visualisation

g <- filtered_graph %>%
  ggraph(layout = "kk") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = degree,
      shape = `Node Type`,
      size = ifelse(node_name %in% c("Sailor Shift"), 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Degree: %s", node_name, genre, notable, release_date, degree
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s<br/>Degree: %s", node_name, genre, notable, release_date, single, degree
        ),
        TRUE ~ sprintf("%s<br/>Degree: %s", node_name, degree)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == "Sailor Shift", "Sailor Shift", NA)
    ),
    fontface = "bold",
    size = 3,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity() + 
  scale_color_gradientn(
    name = "Degree",
    colours = c("#2E3192", "#FFA757"),
    values = scales::rescale(c(0, 13)),
    na.value = "grey50",
    limits = c(0, 13),
    breaks = 0:13
  )

girafe(ggobj = g, width_svg = 9, height_svg = 8)

How has she influenced collaborators of the broader Oceanus Folk community?

• This graph is displays a network overview of all People/Musical Groups who have produced Oceanus Folk Songs/Albums.
• While Sailor Shift is connected to a portion of the Oceanus Folk community, most artists in this genre remain outside her influence network - either as distant connections (3rd to 13th degree) or completely unconnected.
• In conclusion, Sailor Shift has a moderate influence on the broader Oceanus Folk community since her impact is discernible but not widespread.

Other Insights

• It seems that the Oceanus Folk community places a strong emphasis on collaboration
• Rather than simply drawing influence from each other’s work, they prefer collaborating directly
  • Evidenced by the sporadic ‘Influenced By’ connections in the network graph

5 VAST 2025 MC1 Question 2

Develop visualizations that illustrate how the influence of Oceanus Folk has spread through the musical world.

4 Exploratory Data Analysis (EDA) - Sailor’s Network

• In total, Sailor Shift released 38 Songs and Albums from 2024 to 2040
• Details on the Songs and Albums can be found in the table below
• Sailor Shift released 4 Songs in 2028
  • Among those, only 1 is a Single
  • Therefore, the viral Song that spark her breakthrough is High Tide Heartbeat
• Among her Songs and Albums, 36 belong to the Oceanus Folk genre, while there is 1 Americana and 1 Synthwave Album each.

# Step 0: Get name of 'Sailor Shift'
sailor_vertex_name <- mc1_nodes_clean %>%
  filter(is_sailor == TRUE) %>%
  pull(name)

# Step 1: Find outgoing edges from Sailor Shift
sailor_out_edges <- mc1_edges_clean %>%
  filter(from == sailor_vertex_name)

# Step 2: Identify neighbour node names
sailor_out_node_names <- sailor_out_edges$to

# Step 4: Identify songs/albums
sailor_music_all <- mc1_nodes_clean %>%
  filter(name %in% sailor_out_node_names, `Node Type` %in% c("Song", "Album")) %>%
  pull(name)

mc1_nodes_clean %>%
  filter(name %in% sailor_music_all) %>%
  arrange(release_date) %>%
  select(`Node Type`, node_name, release_date, genre, notable, single, notoriety_date) %>%
  kable() %>%
  kable_styling("striped", full_width = F) %>% 
  scroll_box(height = "300px")

Node Type	node_name	release_date	genre	notable	single	notoriety_date
Album	The Kelp Forest Canticles	2024	Oceanus Folk	TRUE	NA	NA
Album	Luminescent Tides	2025	Oceanus Folk	TRUE	NA	NA
Album	Shoreline Sonnets	2026	Oceanus Folk	TRUE	NA	NA
Album	Tidal Pop Waves	2028	Oceanus Folk	TRUE	NA	NA
Song	Chord of the Deep	2028	Oceanus Folk	FALSE	FALSE	NA
Song	Electric Eel Love	2028	Oceanus Folk	TRUE	FALSE	NA
Song	High Tide Heartbeat	2028	Oceanus Folk	FALSE	TRUE	NA
Song	Sun-Drenched Daydream	2028	Oceanus Folk	FALSE	FALSE	NA
Album	Tides of Echos	2029	Oceanus Folk	TRUE	NA	NA
Album	Salty Dreams	2030	Oceanus Folk	TRUE	NA	NA
Song	Driftwood Lullaby	2030	Oceanus Folk	FALSE	FALSE	NA
Song	Heart of the Habitat	2030	Oceanus Folk	FALSE	TRUE	NA
Song	Reef Rhythm	2030	Oceanus Folk	FALSE	FALSE	NA
Song	Seashell Serenade	2030	Oceanus Folk	TRUE	TRUE	2030
Album	Hidden Depths	2031	Oceanus Folk	TRUE	NA	NA
Album	Neon Heartbeat	2031	Synthwave	FALSE	NA	NA
Album	Submerged Sonatas	2031	Oceanus Folk	TRUE	NA	NA
Album	Tidesworn Ballads	2031	Oceanus Folk	TRUE	NA	NA
Album	The Current & The Chord	2032	Oceanus Folk	TRUE	NA	NA
Song	Saltwater Hymn	2032	Oceanus Folk	FALSE	FALSE	NA
Album	Ballads for the End of Time	2033	Oceanus Folk	TRUE	NA	NA
Album	Melancholy Circuitry	2033	Americana	TRUE	NA	NA
Album	Coral Beats	2034	Oceanus Folk	TRUE	NA	NA
Album	Drifting Between the Stars and the Sea	2034	Oceanus Folk	TRUE	NA	NA
Song	Barnacle Heart	2034	Oceanus Folk	FALSE	FALSE	NA
Song	Into the Current	2034	Oceanus Folk	FALSE	TRUE	NA
Song	Moon Over the Tide	2034	Oceanus Folk	TRUE	FALSE	NA
Album	Artificial Sunsets	2035	Oceanus Folk	TRUE	NA	NA
Album	Tides & Ballads	2036	Oceanus Folk	TRUE	NA	NA
Song	Fog & Fiddle	2036	Oceanus Folk	TRUE	FALSE	NA
Song	The Fisherman's Prayer	2036	Oceanus Folk	FALSE	TRUE	NA
Album	Ballads for the Low Tide	2037	Oceanus Folk	TRUE	NA	NA
Album	Electric Reverie	2038	Oceanus Folk	TRUE	NA	NA
Album	Oceanbound	2038	Oceanus Folk	TRUE	NA	NA
Song	Stormsong	2038	Oceanus Folk	TRUE	TRUE	NA
Album	Echoes of the Deep	2040	Oceanus Folk	TRUE	NA	NA
Song	Salt in My Veins	2040	Oceanus Folk	FALSE	FALSE	NA
Song	The Last Mariner	2040	Oceanus Folk	FALSE	FALSE	NA

# Prepare data
sailor_release_data <- mc1_nodes_clean %>%
  filter(name %in% sailor_music_all) %>%
  count(release_date, genre)

# Plot
ggplot(sailor_release_data, aes(x = release_date, y = n, fill = genre)) +
  geom_col() +
  geom_vline(xintercept = 2028, linetype = "dashed", color = "red") +
    annotate("text", 
           x = 2028, 
           y = max(sailor_release_data$n) + 0.5,
           label = "Viral Breakthrough", 
           color = "red", 
           size = 3.5,
           angle = 0,        # <- horizontal
           vjust = -0.5,     # <- moves it slightly above
           hjust = 0.5       # <- centers it horizontally
  ) +
  scale_fill_brewer(palette = "Pastel1") +
  labs(
    title = "Sailor Shift's Music Releases (2024–2040)",
    x = "Year",
    y = "Number of Songs/Albums",
    fill = "Genre"
  ) +
  theme_minimal()

5.1 Question 2a) Was this influence intermittent or did it have a gradual rise?

We will explore these three areas to understand the influence of Oceanus Folk over time:

1. Yearly number of Oceanus Folk Music releases

2. Yearly number of works influenced by Oceanus Folk Music

3. Yearly number of Oceanus Folk Artists, Influenced Artists and Collaborators

5.1.1 Yearly number of Oceanus Folk Music releases

The number of Oceanus Folk Music (Songs/Albums) releases that were released each year are obtained using the codes below:

# Data Preparation

# Step 1: Get all node names in Oceanus Folk genre
oceanus_nodes <- mc1_nodes_clean %>%
  filter(genre == "Oceanus Folk") %>%
  pull(name)

# Step 2: Count number of oceanus folk nodes by release date
oceanus_nodes_by_date <- mc1_nodes_clean %>%
  filter(name %in% unique(oceanus_nodes)) %>%
  count(release_date, name = "oceanus_nodes_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(oceanus_nodes_count))



# Visualisation

plot_ly(
  data = oceanus_nodes_by_date,
  x = ~release_date,
  y = ~oceanus_nodes_count,
  type = "bar",
  name = "Number of Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", oceanus_nodes_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Oceanus Folk Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    ),
    annotations = list(
      list(
        x = 2024, 
        y = 280,
        text = "<b>2024: Sailor Shift's Debut</b>",
        xref = "x", yref = "y",
        xanchor = "right",
        showarrow = TRUE, arrowhead = 2,
        ax = -30, ay = -40,
        font = list(color="#2E3192", size=12)
      ),
      list(
        x = 2028, 
        y = 320,
        text = "<b>2028: Sailor Shift's Breakthrough</b>",
        xref = "x", yref = "y",
        xanchor = "right",
        showarrow = TRUE, arrowhead = 2,
        ax = -30, ay = -40,
        font = list(color="#2E3192", size=12)
      )
    ),
    shapes = list(
      list(
        type = "line",
        x0 = 2024, x1 = 2024,
        y0 = 0, y1 = 280,
        line = list(dash="dash", color="grey")
      ),
      list(
        type = "line",
        x0 = 2028, x1 = 2028,
        y0 = 0, y1 = 320,
        line = list(dash="dash", color="grey")
      )
    )
  )

Insights

The chart illustrates the rise of Oceanus Folk music from its origins in 1992 to 2040. The blue bars represent the number of Oceanus Folk songs or albums released each year, highlighting notable spikes especially in the mid-2020s.

The red line shows the cumulative total of Oceanus Folk releases over time, which demonstrates a steady and sustained growth in Oceanus Folk music. This growth before plateauing in 2040, indicating that the genre has matured.

Two key milestones in Sailor Shift’s career - her solo debut in 2024 and breakthrough in 2028 were also mapped in the chart. The significant increases in Oceanus Folk music releases occurred in 2023 and 2026, which were the years immediately before and after her key milestones. This suggests that Sailor Shift’s emergence coincided with a rising wave of Oceanus Folk music, and that her success helped to fuel the genre’s growth in the subsequent years.

5.1.2 Influence based on number of works that were influenced by Oceanus Folk music

To measure how Oceanus Folk influenced the broader music landscape, we tracked how often songs and albums from other genres were influenced by Oceanus Folk works. These influence relationships were drawn from edge types consisting InStyleOf, CoverOf, InterpolatesFrom, LyricalReferenceTo, and DirectlySamples.

We then focused on edges where an Oceanus Folk song or album is the target (to), and the source (from) is the influenced work. The release date of each influenced work and number of such works for each year were extracted. To reveal long-term trends, cumulative total of influenced works are calculated.

The above approach is reflected in the codes below:

# Data Preparation

# Step 1: Count number of influenced music by release date
influence_yearly <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(song_to %in% unique(oceanus_nodes),
         `Edge Colour` == "Influenced By",
         infuence_music_collaborate != song_to) %>%
  distinct(infuence_music_collaborate, influence_release_date) %>%
  count(influence_release_date, name = "num_influenced_nodes") %>%
  complete(influence_release_date = 1975:2040, fill = list(num_influenced_nodes = 0)) %>%
  arrange(influence_release_date) %>%  # Ensure dates are in chronological order
  filter(cumsum(num_influenced_nodes) > 0) %>%
  mutate(cumulative_influenced = cumsum(num_influenced_nodes))



# Visualisation

plot_ly(
  data = influence_yearly,
  x = ~influence_release_date,
  y = ~num_influenced_nodes,
  type = "bar",
  name = "Number of Influenced Songs/Albums",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0("Year: ", influence_release_date, "<br>Influenced: ", num_influenced_nodes)
) %>%
add_trace(
  y = ~cumulative_influenced,
  type = "scatter",
  mode = "lines+markers",
  name = "Cumulative Influenced",
  line = list(color = "black", width = 2),
  marker = list(color = "red", size = 6),
  hoverinfo = "text",
  hovertext = ~paste0("Year: ", influence_release_date, "<br>Cumulative: ", cumulative_influenced)
) %>%
layout(
  title = "Yearly Oceanus Folk Music Influence on Songs/Albums",
  margin = list(b = 80, t = 80),
  xaxis = list(
  title = NA,
  dtick = 5,
  automargin = TRUE,
  range = c(1990, 2040)
),
  yaxis = list(
  title = "Count",
  automargin = TRUE,
  range = c(0, 300)
),
  legend = list(
    orientation = "h",
    x = 0.5,
    xanchor = "center",
    y = -0.1
  ),
  annotations = list(
    list(
      x = 2024,
      y = 250,
      text = "<b>2024: Sailor Shift's Debut</b>",
      xref = "x", yref = "y",
      xanchor = "right",
      showarrow = TRUE, arrowhead = 2,
      ax = -30, ay = -40,
      font = list(color = "#2E3192", size = 12)
    ),
    list(
      x = 2028,
      y = 280,
      text = "<b>2028: Sailor Shift's Breakthrough</b>",
      xref = "x", yref = "y",
      xanchor = "right",
      showarrow = TRUE, arrowhead = 2,
      ax = -30, ay = -40,
      font = list(color = "#2E3192", size = 12)
    )
  ),
  shapes = list(
    list(
      type = "line",
      x0 = 2024, x1 = 2024,
      y0 = 0, y1 = 280,
      line = list(dash = "dash", color = "grey")
    ),
    list(
      type = "line",
      x0 = 2028, x1 = 2028,
      y0 = 0, y1 = 320,
      line = list(dash = "dash", color = "grey")
    )
  )
)

Insights

This chart shows the rise of Oceanus Folk music as a source of influence in the music landscape over time since it was introduced in 1992. The blue bars represent the number of influence works each year and the red line shows the cumulative total showing long-term trends.

From the 1990s through early 2000s, Oceanus Folk had very few influenced work. From 2010s onwards, Oceanus Folk music became more influential, followed by several spikes between 2010 and 2022, showing growing influence of the genre before Sailor Shift’s debut.

In 2023, there was a sharp increase in both the number of yearly influenced works and the cumulative total. The rising wave of Oceanus Folk influenced music had coincided with Sailor Shift’s rise and the influence continued to accelerate with Sailor’s breakthrough until 2029.

After 2030, the influence tapers off, suggesting the genre may have matured or plateaued.

5.1.3 Influence based on number of Oceanus Folk Artists and Influenced Artists

We examined the network of people and groups involved in Oceanus Folks music creation and influence. Our analysis begins by identifying all artists (i.e. Person and Musical Group) directly connected to Oceanus Folk music via these edges (PerformerOf, ComposerOf , ProducerOf, LyricistOf ) for Oceanus Folk songs and albums. We also added members of musical groups (i.e. collaborators) that contributed to Oceanus Folk works.

In parallel, we traced those influenced by Oceanus Folk: people and groups who created songs or albums influenced by Oceanus works music (i.e. with edges belonging to InStyleOf, InterpolatesFrom, CoverOf, LyricalReferenceTo and DirectlySamples). Similarly, members of musical groups (i.e. collaborators) that created works influenced by Oceanus Folk works were included.

Together, these artists form a comprehensive view of Oceanus Folk’s influence on the music landscape.

# Step 1: Count number of influenced artists by release date
creators_by_date <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(song_to %in% unique(oceanus_nodes),
         influence_creator != creator_from) %>%
  # Get unique artist-date pairs first
  distinct(influence_creator, influence_release_date) %>%
  # Find first influence date for each artist
  group_by(influence_creator) %>%
  summarize(
    first_influence_date = if(n() > 0) min(influence_release_date) else NA_real_,
    .groups = "drop"
  ) %>%
  # Count new artists by first influence date
  count(first_influence_date, name = "people_count") %>%
  arrange(first_influence_date) %>%
  rename(influence_release_date = first_influence_date) %>%
  # Calculate cumulative unique artists
  mutate(cumulative_count = cumsum(people_count))



# Visualisation

plot_ly(
  data = creators_by_date,
  x = ~influence_release_date,
  y = ~people_count,
  type = "bar",
  name = "Number of Artists",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", influence_release_date,
    "<br>Count: ", people_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", influence_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Number of New Oceanus Folk Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    ),
    annotations = list(
      list(
        x = 2024, 
        y = 800,
        text = "<b>2024: Sailor Shift's Debut</b>",
        xref = "x", yref = "y",
        xanchor = "right",
        showarrow = TRUE, arrowhead = 2,
        ax = -30, ay = -40,
        font = list(color="#2E3192", size=12)
      ),
      list(
        x = 2028, 
        y = 1120,
        text = "<b>2028: Sailor Shift's Breakthrough</b>",
        xref = "x", yref = "y",
        xanchor = "right",
        showarrow = TRUE, arrowhead = 2,
        ax = -30, ay = -40,
        font = list(color="#2E3192", size=12)
      )
    ),
    shapes = list(
      list(
        type = "line",
        x0 = 2024, x1 = 2024,
        y0 = 0, y1 = 800,
        line = list(dash="dash", color="grey")
      ),
      list(
        type = "line",
        x0 = 2028, x1 = 2028,
        y0 = 0, y1 = 1120,
        line = list(dash="dash", color="grey")
      )
    )
  )

Insights

This chart shows the rise of Oceanus Folk music as a source of influence in the music landscape over time since it was introduced in 1992. The blue bars represent the number of influence works each year and the red line shows the cumulative total showing long-term trends.

From the 1990s through early 2000s, Oceanus Folk had very few influenced work. From 2010s onwards, Oceanus Folk music became more influential, followed by several spikes between 2010 and 2022, showing growing influence of the genre before Sailor Shift’s debut.

In 2023, there was a sharp increase in both the number of yearly influenced works and the cumulative total. The rising wave of Oceanus Folk influenced music had coincided with Sailor Shift’s rise and the influence continued to accelerate with Sailor’s breakthrough until 2029.

After 2030, the influence tapers off, suggesting the genre may have matured or plateaued.

# Step 2: Select and rename columns
df1 <- oceanus_nodes_by_date %>%
  select(year = release_date, value = cumulative_count) %>%
  mutate(series = "Music Releases")

df2 <- influence_yearly %>%
  select(year = influence_release_date, value = cumulative_influenced) %>%
  mutate(series = "Influenced Songs/Albums")

df3 <- creators_by_date %>%
  select(year = influence_release_date, value = cumulative_count) %>%
  mutate(series = "New Influenced Artists")

# Step 3: Combine into one tidy data frame
combined_df <- bind_rows(df1, df2, df3)

# Step 4: Plot
plot_ly(combined_df,
        x = ~year,
        y = ~value,
        color = ~series,
        colors = c("Music Releases" = "#ADD8E6", 
                   "Influenced Songs/Albums" = "#F08080", 
                   "New Influenced Artists" = "#C2E0C6"),
        type = 'scatter',
        mode = 'lines+markers',
        hoverinfo = "text",
        hovertext = ~paste0("Year: ", year, "<br>", series, ": ", value)
) %>%
  layout(
    title = list(text = "Oceanus Folk Influence Over Time (Cumulative)", x = 0.5),
    xaxis = list(title = list(text = ""), dtick = 5),
    yaxis = list(title = "Cumulative Count"),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = 1.15,         # position legend above the plot
      yanchor = "bottom"
    ),
  margin = list(t = 160, b = 80),

    # Add annotations
    annotations = list(
      list(
        x = 2024, 
        y = 800,
        text = "<b>2024: Sailor Shift's Debut</b>",
        xref = "x", yref = "y",
        xanchor = "right",
        showarrow = TRUE,
        arrowhead = 2,
        ax = -30, ay = -40,
        font = list(color = "#2E3192", size = 12)
      ),
      list(
        x = 2028, 
        y = 1120,
        text = "<b>2028: Sailor Shift's Breakthrough</b>",
        xref = "x", yref = "y",
        xanchor = "right",
        showarrow = TRUE,
        arrowhead = 2,
        ax = -30, ay = -40,
        font = list(color = "#2E3192", size = 12)
      )
    ),

    # Add vertical dashed lines
    shapes = list(
      list(
        type = "line",
        x0 = 2024, x1 = 2024,
        y0 = 0, y1 = 800,
        line = list(dash = "dash", color = "grey")
      ),
      list(
        type = "line",
        x0 = 2028, x1 = 2028,
        y0 = 0, y1 = 1120,
        line = list(dash = "dash", color = "grey")
      )
    )
  )

# Step 1: Prepare tidy data frame with yearly counts (not cumulative)
df1_counts <- oceanus_nodes_by_date %>%
  select(year = release_date, value = oceanus_nodes_count) %>%
  mutate(series = "Music Releases")

df2_counts <- influence_yearly %>%
  select(year = influence_release_date, value = num_influenced_nodes) %>%
  mutate(series = "Influenced Songs/Albums")

df3_counts <- creators_by_date %>%
  select(year = influence_release_date, value = people_count) %>%
  mutate(series = "New Influenced Artists")

combined_counts_df <- bind_rows(df1_counts, df2_counts, df3_counts)

# Step 2: Create faceted bar plot with horizontal layout
p <- ggplot(combined_counts_df, aes(x = year, y = value, fill = series)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~series, nrow = 1, scales = "fixed") +  # horizontal facets
  scale_fill_manual(values = c("Music Releases" = "#ADD8E6", 
                               "Influenced Songs/Albums" = "#F08080", 
                               "New Influenced Artists" = "#C2E0C6")) +
  labs(title = "Yearly Numbers by Category",
       x = NULL, y = "Yearly Count") +
  theme_minimal()+
  theme(legend.position = "none", panel.spacing = unit(1.5, "lines"), plot.title = element_text(hjust = 0.5))  # fully removes legend

# Step 3: Make it interactive
ggplotly(p)

5.1.3.1 Genre Star Factor

# Step 1: First get the song-level influence data (similar to your Step 10)
song_influence_lists <- creator_and_songs_and_influences_and_creators_collaborate %>%
  group_by(song_to, song_name, creator_release_date, song_genre, notable) %>%
  distinct () %>%
  summarize(
    unique_collaborate = list(unique(na.omit(infuence_music_collaborate[creator_from != influence_creator & `Edge Colour` == "Creator Of"]))),
    unique_influence_creators = list(unique(na.omit(influence_creator[creator_from != influence_creator & `Edge Colour` == "Influenced By"]))),
    unique_influence_music = list(unique(na.omit(infuence_music_collaborate[creator_from != influence_creator & !is.na(influence_genre)])))
  )

# Step 2: Aggregate statistics by genre
genre_stats <- song_influence_lists %>%
  group_by(song_genre) %>%
  summarize(
    total_songs = n_distinct(song_to),
    notable_hits = sum(notable == TRUE, na.rm = TRUE),
    collaboration = length(unique(unlist(unique_collaborate))),
    influence_creators = length(unique(unlist(unique_influence_creators))),
    collaboration_influence_creator = length(unique(c(unlist(unique_influence_creators),unlist( unique_collaborate)))),
    influence_music = length(unique(unlist(unique_influence_music)))
  )

# Step 12: Create Scoring Rubric
genre_rankings <- genre_stats %>%
  mutate(
    # Normalize each metric (0-1 scale)
    songs_score = (total_songs - min(total_songs)) / (max(total_songs) - min(total_songs)),
    notable_score = (notable_hits - min(notable_hits)) / (max(notable_hits) - min(notable_hits)),
    artists_score = (collaboration_influence_creator - min(collaboration_influence_creator)) / (max(collaboration_influence_creator) - min(collaboration_influence_creator)),
    music_score = (influence_music - min(influence_music)) / (max(influence_music) - min(influence_music)),
    
    # Calculate composite score (equal weighting)
    composite_score = songs_score + notable_score + artists_score + music_score
  ) %>%
  arrange(desc(composite_score)) %>%
  select(song_genre, total_songs, notable_hits, collaboration_influence_creator, influence_music, composite_score)

# Step 13: Final Ranked Table
genre_rankings %>%
  mutate(
    Rank = row_number(),
    `Star Factor` = round(composite_score, 2)
  ) %>%
  select(Rank, song_genre, total_songs, notable_hits, collaboration_influence_creator, 
         influence_music, `Star Factor`) %>%
  rename(
    `Genre` = song_genre,
    `Total Music` = total_songs,
    `Notable Hits` = notable_hits,
    `Artist Influ & Colab` = collaboration_influence_creator,
    `Music Influenced` = influence_music
  ) %>%
  kable(caption = "Genre Ranked by Star Factor") %>%
  kable_styling("striped", full_width = F) %>%
  scroll_box(height = "300px")

Genre Ranked by Star Factor

Rank	Genre	Total Music	Notable Hits	Artist Influ & Colab	Music Influenced	Star Factor
1	Dream Pop	742	709	3254	589	3.91
2	Indie Folk	450	432	2645	603	2.95
3	Doom Metal	348	335	2614	646	2.73
4	Synthwave	382	364	2266	496	2.48
5	Oceanus Folk	305	244	1168	244	1.48
6	Psychedelic Rock	172	168	1518	277	1.35
7	Indie Rock	208	196	1211	248	1.30
8	Alternative Rock	258	251	793	226	1.28
9	Southern Gothic Rock	242	231	645	285	1.28
10	Americana	184	173	1043	165	1.05
11	Indie Pop	134	122	414	199	0.77
12	Desert Rock	125	120	826	107	0.74
13	Jazz Surf Rock	127	123	641	120	0.71
14	Space Rock	121	113	696	81	0.64
15	Lo-Fi Electronica	136	127	218	127	0.61
16	Darkwave	104	97	545	102	0.58
17	Blues Rock	109	103	393	122	0.58
18	Post-Apocalyptic Folk	72	66	502	88	0.46
19	Symphonic Metal	64	56	491	88	0.43
20	Emo/Pop Punk	77	72	182	95	0.39
21	Avant-Garde Folk	70	64	185	97	0.37
22	Speed Metal	84	76	160	73	0.36
23	Synthpop	46	41	310	52	0.28
24	Sea Shanties	21	4	4	0	0.01
25	Acoustic Folk	18	7	3	0	0.01
26	Celtic Folk	12	3	5	1	0.00

(didnt copy over combined plot)

5.1.4 Bayesian Surprise

We have analysed the Oceanus Folks music influence trends over time using line and bar charts. Besides visual plots, we can quantify if the influence was intermittent or gradual using the statistical tool - Bayesian Surprise.

Bayesian Surprise is a concept in information theory, which is used to identify moments of unexpected change in sequential data. Based our research, it is suitable for analysing trends like the spread of musical influence and temporal pattern detection.

To compute Bayesian Surprise, we treat each year as a new observation and compare the current year’s count to the prior expectation which is modelled using a Poisson probability distribution which is suitable for our count data across years.

These are the steps in generating the Surprise Score:

• Obtain the yearly time series data from the previous 3 analysis.

• Model prior belief based on previous year(s)

• Compute posterior with the new data

• Calculate Bayesian Surprise as the KL divergence (Kullback–Leibler divergence) between the prior and posterior

The Surprise Computation Function is defined using the following codes:

library(zoo)

# Define general function to compute Bayesian Surprise
compute_bayesian_surprise <- function(data, year_col, count_col, window = 5) {
  data <- data %>%
    rename(year = {{year_col}}, count = {{count_col}}) %>%
    complete(year = min(year):max(year), fill = list(count = 0)) %>%
    arrange(year)

  # Calculate moving average (prior belief)
  data <- data %>%
    mutate(prior_mean = rollmean(count, k = window, align = "right", fill = NA))

  # Compute KL Divergence for Poisson distributions
  data <- data %>%
    mutate(
      surprise = ifelse(
        !is.na(prior_mean) & prior_mean > 0 & count > 0,
        count * log(count / prior_mean) - (count - prior_mean),
        NA
      )
    )

  return(data)
}

Bayesian Surprise is computed using KL Divergence between each year’s observed value and a rolling average of the previous five years, treating yearly counts as Poisson-distributed events using the following:

# 1. Surprise for Oceanus Folk releases
surprise_releases <- compute_bayesian_surprise(
  oceanus_nodes_by_date,
  release_date,
  oceanus_nodes_count,
  window = 5
)

# 2. Surprise for influenced works
surprise_influence <- compute_bayesian_surprise(
  influence_yearly,
  influence_release_date,
  num_influenced_nodes,
  window = 5
)

# 3. Surprise for artists and contributors
surprise_contributors <- compute_bayesian_surprise(
  creators_by_date,
  influence_release_date,
  people_count,
  window = 5
)

Bayesian Surprise Across Oceanus Folk Music Releases, Influenced Works, and Artists

plot_data <- bind_rows(
  surprise_releases %>% select(year, surprise) %>% mutate(type = "Music Releases"),
  surprise_influence %>% select(year, surprise) %>% mutate(type = "Influenced Works"),
  surprise_contributors %>% select(year, surprise) %>% mutate(type = "Artists")
) %>%
  filter(!is.na(surprise), year >= 1990)

# Plot using Plotly
plot_ly(data = plot_data, x = ~year, y = ~surprise, color = ~type, colors = "Set1",
        type = 'scatter', mode = 'lines+markers',
        hoverinfo = "text",
        hovertext = ~paste0("Year: ", year,
                            "<br>Category: ", type,
                            "<br>Surprise: ", round(surprise, 2))) %>%
  layout(
    title = "Bayesian Surprise Across Oceanus Folk Music Releases, Influenced Works, and Artists",
    xaxis = list(title = "Year", dtick = 5, range = c(1990, max(plot_data$year))),
    yaxis = list(title = "Surprise Score (KL Divergence)"),
    legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.2),
    margin = list(t = 60, b = 80)
  )

unique(plot_data$type)

[1] "Music Releases"   "Influenced Works" "Artists"         

plot_data <- plot_data %>%
  mutate(type = case_when(
    type == "Music Releases"    ~ "Music Releases",
    type == "Influenced Works"  ~ "Influenced Songs/Albums",
    type == "Artists"           ~ "New Influenced Artists",
    TRUE                        ~ type
  ))

plot_ly(
  data = plot_data, 
  x = ~year, 
  y = ~surprise, 
  color = ~type,
  colors = c(
    "Music Releases"          = "#ADD8E6", 
    "Influenced Songs/Albums" = "#F08080", 
    "New Influenced Artists"  = "#C2E0C6"
  ),
  type = 'scatter', 
  mode = 'lines+markers',
  hoverinfo = "text",
  hovertext = ~paste0(
    "Year: ", year,
    "<br>Category: ", type,
    "<br>Surprise: ", round(surprise, 2)
  )
) %>%
layout(
  legend = list(
    orientation = "h",
    x = 0.5,
    xanchor = "center",
    y = 1.1,
    yanchor = "bottom",
    font = list(size = 12)
  ),
  title = list(
    text = "Bayesian Surprise (Oceanus Folk Influenced Songs/Albums, Music Releases, and New Influenced Artists)",
    x = 0.5,
    xanchor = "center"
  ),
  xaxis = list(title = "Year", dtick = 5, range = c(1990, max(plot_data$year))),
  yaxis = list(title = "Surprise Score (KL Divergence)"),
  margin = list(t = 160, b = 80),
  
     # Add annotations
    annotations = list(
      list(
        x = 2024, 
        y = 100,
        text = "<b>2024: Sailor Shift's Debut</b>",
        xref = "x", yref = "y",
        xanchor = "right",
        showarrow = TRUE,
        arrowhead = 2,
        ax = -30, ay = -40,
        font = list(color = "#2E3192", size = 12)
      ),
      list(
        x = 2028, 
        y = 120,
        text = "<b>2028: Sailor Shift's Breakthrough</b>",
        xref = "x", yref = "y",
        xanchor = "right",
        showarrow = TRUE,
        arrowhead = 2,
        ax = -30, ay = -40,
        font = list(color = "#2E3192", size = 12)
      )
    ),

    # Add vertical dashed lines
    shapes = list(
      list(
        type = "line",
        x0 = 2024, x1 = 2024,
        y0 = 0, y1 = 100,
        line = list(dash = "dash", color = "grey")
      ),
      list(
        type = "line",
        x0 = 2028, x1 = 2028,
        y0 = 0, y1 = 120,
        line = list(dash = "dash", color = "grey")
      )
    )
  )

Insights

The Bayesian Surprise analysis showed that Oceanus Folk’s influence was intermittent rather than a smooth, gradual rise. While cumulative trends in music releases, influenced works and artist numbers may suggest steady growth, Bayesian Surprise uncovers a different insight that there were indeed intermittent surges of activity.

For example, the sharp peaks in surprise scores for artists (red line) in years like 2004, 2010, 2017, and especially 2023 suggest that Oceanus Folk influence had waves of breakthroughs and not slow accumulation.

Influenced works (blue line) increased in 2017, spiked in 2023 and sustained its increase across 2030 to 2033, which shows how Sailor’s debut and breakthrough had catalysed Oceanus Folk genre influence.

Music releases (green line) generally showed lower surprise, which confirms that number of Oceanus Folks music was not a key driver to influence.

Thus, while the overall trend shows long-term growth, Bayesian Surprise confirms that Oceanus Folk’s rise was intermittent and driven by burst such as artist breakthrough. Such bursts also maintained the momentum of the influence for an interval of time.

5.2 Question 2b) What genres and top artists have been most influenced by Oceanus Folk?

5.2.1 What genres have been most influenced by Oceanus Folk?

To determine which genres have been most influenced by Oceanus Folk, all songs and albums were identified. Then, the music (Songs/Albums) that influenced them were obtained to calculate the frequency and percentage of Oceanus Folk’s influence across different musical genre. This analysis reveals the genres that show the strongest impact from Oceanus Folk’s musical style.

genre_influence_stats <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(infuence_music_collaborate != song_to) %>%
  distinct(song_to, song_genre, infuence_music_collaborate, influence_genre, `Edge Colour`) %>%
  group_by(song_genre) %>%
  summarize(
    total_music = n_distinct(song_to),
    total_influences = n_distinct(na.omit(infuence_music_collaborate[!is.na(influence_genre)])),
    oceanus_influences = n_distinct(na.omit(infuence_music_collaborate[influence_genre == "Oceanus Folk"])),
    other_influences = total_influences - oceanus_influences,
    perc_oceanus = round(oceanus_influences / total_influences * 100, 1),
    no_influences = sum(is.na(influence_genre)),
  ) %>%
  arrange(desc(oceanus_influences))

genre_influence_stats %>%
  kable(caption = "Genre Ranked by Oceanus Influence") %>%
  kable_styling("striped", full_width = F) %>%
  scroll_box(height = "300px")

Genre Ranked by Oceanus Influence

song_genre	total_music	total_influences	oceanus_influences	other_influences	perc_oceanus	no_influences
Oceanus Folk	305	244	97	147	39.8	1058
Indie Folk	450	603	93	510	15.4	1938
Synthwave	382	496	16	480	3.2	1577
Doom Metal	348	646	14	632	2.2	1515
Dream Pop	742	591	14	577	2.4	3506
Synthpop	46	52	10	42	19.2	154
Psychedelic Rock	172	277	9	268	3.2	921
Alternative Rock	258	227	8	219	3.5	745
Indie Rock	208	248	8	240	3.2	799
Desert Rock	125	107	7	100	6.5	585
Americana	184	165	5	160	3.0	787
Blues Rock	109	123	4	119	3.3	313
Southern Gothic Rock	242	286	4	282	1.4	624
Symphonic Metal	64	88	3	85	3.4	277
Avant-Garde Folk	70	97	2	95	2.1	161
Post-Apocalyptic Folk	72	88	2	86	2.3	308
Space Rock	121	81	2	79	2.5	555
Celtic Folk	12	1	1	0	100.0	21
Emo/Pop Punk	77	96	1	95	1.0	192
Indie Pop	134	200	1	199	0.5	362
Jazz Surf Rock	127	121	1	120	0.8	491
Lo-Fi Electronica	136	127	1	126	0.8	390
Acoustic Folk	18	0	0	0	NaN	26
Darkwave	104	102	0	102	0.0	444
Sea Shanties	21	0	0	0	NaN	33
Speed Metal	84	74	0	74	0.0	254

library(networkD3)

# Step 1: Prepare data — treat Oceanus Folk as source and include self-influence
sankey_df <- genre_influence_stats %>%
  mutate(
    source = "Oceanus Folk",
    raw_target = ifelse(song_genre == "Oceanus Folk", "Oceanus Folk (in-genre influence)", song_genre),
    value = oceanus_influences,
    target = paste0(raw_target, " (", value, ")")  # ← append the number
  ) %>%
  select(source, target, value) %>%
  arrange(desc(value)) %>%
  head(22)

# Step 2: Create unique nodes
nodes <- data.frame(name = unique(c(sankey_df$source, sankey_df$target)))

# Step 3: Convert names to indices
links <- sankey_df %>%
  mutate(
    source = match(source, nodes$name) - 1,
    target = match(target, nodes$name) - 1
  )

# Step 4: Plot Sankey (Oceanus Folk on the left)
sankeyNetwork(
  Links = links,
  Nodes = nodes,
  Source = "source",
  Target = "target",
  Value = "value",
  NodeID = "name",
  fontSize = 13,
  nodeWidth = 30,
  sinksRight = TRUE  # Flow goes left (source) to right (target)
)

What genres have been most influenced by Oceanus Folk?

• The table above show the top 10 genres who was most influenced by Oceanus Folk.
• The Oceanus Folk genre has been the most influenced by itself with about 40% of all music (Songs/Albums) drawing inspiration from Oceanus Folk music.

5.2.2 Which top artists have been most influenced by Oceanus Folk?

To identify the top artists most influenced by Oceanus Folk, all artists (persons or musical groups) who either (a) created Oceanus Folk music (songs/albums) or (b) were influenced by the genre were identified. Then, all music produced by these artists, along with the musical works that influenced their creations, was counted to reveal those who were most influenced.

creator_influenced_by_stats <- creator_and_songs_and_influenced_by_creator %>%
  distinct(creator_name, creator_node_type, song_to, song_genre, influenced_by, influenced_by_genre, influenced_by_creator, notable) %>%
  group_by(creator_name, creator_node_type) %>%
  summarize(
    total_music = n_distinct(song_to),
    notable_hits = n_distinct(song_to[notable == TRUE]),
    oceanus_music = n_distinct(song_to[song_genre == "Oceanus Folk"]),
    oceanus_influenced_by = n_distinct(na.omit(influenced_by[influenced_by_genre == "Oceanus Folk" & creator_name != influenced_by_creator])),
    total_oceanus_influence = oceanus_music + oceanus_influenced_by
  ) %>%
  arrange(desc(total_oceanus_influence)) %>%
  filter(creator_node_type == "Person", notable_hits > 10) %>%
  select(-creator_node_type)

creator_influenced_by_stats %>%
  head(10) %>%
  rename(
    `Artist` = creator_name,
    `Total Music` = total_music,
    `Notable Hits` = notable_hits,
    `No. of Oceanus Folk Music` = oceanus_music,
    `Oceanus Folk Influence` = oceanus_influenced_by,
    `Oceanus Folk Music & Influence` = total_oceanus_influence
  ) %>%
  kable(caption = "Ranking of Oceanus Folk Influence on Artists") %>%
  kable_styling("striped", full_width = F) %>%
  scroll_box(height = "200px")

Ranking of Oceanus Folk Influence on Artists

Artist	Total Music	Notable Hits	No. of Oceanus Folk Music	Oceanus Folk Influence	Oceanus Folk Music & Influence
Sailor Shift	38	25	36	0	36
Min He	13	13	1	10	11
Na Ren	13	13	3	7	10
Qiang Song	12	11	3	5	8
Min Fu	12	12	0	7	7
Ping Zeng	11	11	1	6	7
Xiuying Huang	14	14	2	5	7
Chao Qiu	12	12	2	4	6
Chao Wu	13	12	4	2	6
Yan Qin	13	13	0	6	6

# Step 1: Prepare data — treat Oceanus Folk as source and include self-influence
sankey_df <- creator_influenced_by_stats %>%
  mutate(
    source = "Oceanus Folk",
    raw_target = creator_name,
    value = total_oceanus_influence,
    target = paste0(raw_target, " (", value, ")")  # ← append the number
  ) %>%
  select(source, target, value) %>%
  arrange(desc(value)) %>%
  head(10)

# Step 4: Nodes (no need for group column)
nodes <- data.frame(name = unique(c(sankey_df$source, sankey_df$target)))

# Step 5: Convert names to indices
links <- sankey_df %>%
  mutate(
    source = match(source, nodes$name) - 1,
    target = match(target, nodes$name) - 1
  )

# Step 6: Plot Sankey (same as before)
sankeyNetwork(
  Links = links,
  Nodes = nodes,
  Source = "source",
  Target = "target",
  Value = "value",
  NodeID = "name",
  fontSize = 13,
  nodeWidth = 30,
  sinksRight = TRUE,
  height = 600,
  width = 1000
)

Which top artists have been most influenced by Oceanus Folk?

• The table above show the top 10 artists who was most influenced by Oceanus Folk
  • These are artists who have produced Oceanus Folk music or have been influenced by it.
  • Top artists are defined by having more than 10 notable hits.
  • These artists are rank by the amount that Oceanus Folk has influenced them.
• The top artist is Sailor Shift who has produced 36 Oceanus Folk songs.
• The next artist is Min He, who has produced one Oceanus Folk song and has been influenced by 10 other Oceanus Folk songs, bringing the total to 11 songs.

5.3 Question 2c) On the converse, how has Oceanus Folk changed with the rise of Sailor Shift? From which genres does it draw most of its contemporary inspiration?

We will examine the diversity of musical genres that has influenced and been influenced by Oceanus Folk over time. This can be measured by Genre Entropy, which comes from information theory where higher entropy means more variety and unpredictability, while lower entropy means concentration in fewer genres, as explained by Ferwerda and Schedl (2016) in their research paper.

For our case, we will compute:

Incoming entropy = How diverse other genres were in influencing Oceanus Folk music in a given year (i.e. what genres inspired new Oceanus Folk music)

Outgoing entropy = How diverse the genres were which Oceanus Folk had influenced (i.e. what range of genres borrowed from Oceanus Folk).

By plotting both directions of entropy over time using a mirrored bar chart and mapping it against Sailor’s debut and breakthrough, we can visualise the change and impact clearly.

# Step 1: Filter influence edges
influence_edges <- mc1_edges_clean %>%
  filter(`Edge Type` %in% c("InStyleOf", "CoverOf", "InterpolatesFrom", 
                            "LyricalReferenceTo", "DirectlySamples"))

# Step 2: Join genre and release date info
influence_genres <- influence_edges %>%
  left_join(mc1_nodes_clean %>% select(name, genre), by = c("from" = "name")) %>%
  rename(source_genre = genre) %>%
  left_join(mc1_nodes_clean %>% select(name, genre, release_date), by = c("to" = "name")) %>%
  rename(target_genre = genre, to_release = release_date) %>%
  left_join(mc1_nodes_clean %>% select(name, release_date), by = c("from" = "name")) %>%
  rename(from_release = release_date)

# Step 3a: Incoming entropy (genres influencing Oceanus Folk)
incoming_entropy_yearly <- influence_genres %>%
  filter(target_genre == "Oceanus Folk", !is.na(source_genre), !is.na(to_release)) %>%
  mutate(year = as.integer(to_release)) %>%
  group_by(year, source_genre) %>%
  summarise(count = n(), .groups = "drop") %>%
  group_by(year) %>%
  mutate(p = count / sum(count)) %>%
  summarise(entropy = entropy::entropy(p, unit = "log2")) %>%
  mutate(direction = "Incoming")

# Step 3b: Outgoing entropy (genres that Oceanus Folk influenced)
outgoing_entropy_yearly <- influence_genres %>%
  filter(source_genre == "Oceanus Folk", !is.na(target_genre), !is.na(from_release)) %>%
  mutate(year = as.integer(from_release)) %>%
  group_by(year, target_genre) %>%
  summarise(count = n(), .groups = "drop") %>%
  group_by(year) %>%
  mutate(p = count / sum(count)) %>%
  summarise(entropy = entropy::entropy(p, unit = "log2")) %>%
  mutate(direction = "Outgoing")

# Step 4: Combine and flip outgoing for mirrored effect
entropy_yearly <- bind_rows(
  incoming_entropy_yearly,
  outgoing_entropy_yearly %>% mutate(entropy = -entropy)
)

# Step 5: Plot mirrored entropy over time with hover text
entropy_plot <- ggplot(entropy_yearly, aes(
  x = year, y = entropy, fill = direction,
  text = paste0(
    "Year: ", year,
    "\nDirection: ", direction,
    "\nEntropy: ", round(abs(entropy), 3), " bits"
  )
)) +
  geom_col(width = 0.8) +
  geom_hline(yintercept = 0, color = "black") +
  scale_fill_manual(
    name = "Entropy Direction",
    values = c("Incoming" = "lightblue", "Outgoing" = "darkblue")
  ) +
  scale_y_continuous(
    breaks = seq(-4, 4, by = 1),
    labels = abs(seq(-4, 4, by = 1)),
    limits = c(-4, 4)
  ) +
  labs(
    title = "Genre Entropy of Oceanus Folk Over Time",
    x = "Year",
    y = "Genre Entropy (bits)"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom")

# Add annotations after setting `max_entropy_val`
max_entropy_val <- max(abs(entropy_yearly$entropy), na.rm = TRUE)

entropy_plot <- entropy_plot +
  geom_vline(xintercept = 2024, linetype = "dashed", color = "grey50", linewidth = 0.7) +
  geom_vline(xintercept = 2028, linetype = "dashed", color = "grey50", linewidth = 0.7) +
  annotate("text", x = 2024.5, y = max_entropy_val + 0.4,
           label = "2024: Sailor Shift's Debut",
           color = "#2E3192", fontface = "bold", size = 3.5, hjust = 1) +
  annotate("text", x = 2028.5, y = max_entropy_val + 0.8,
           label = "2028: Sailor Shift's Breakthrough",
           color = "#2E3192", fontface = "bold", size = 3.5, hjust = 1) +
  annotate("segment", x = 2024.5, y = max_entropy_val + 0.4,
           xend = 2024, yend = max_entropy_val - 0.2,
           arrow = arrow(length = unit(0.2, "cm")), color = "grey50") +
  annotate("segment", x = 2028.5, y = max_entropy_val + 0.8,
           xend = 2028, yend = max_entropy_val - 0.2,
           arrow = arrow(length = unit(0.2, "cm")), color = "grey50")

# Use plotly for interactivity
ggplotly(entropy_plot, tooltip = "text") %>%
  layout(legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.2))

Insights

Before 2013: Limited Influence during earlier years

During the early years of Oceanus Folk, both incoming and outgoing entropy remained sparse, indicating limited cross-genre interaction. However, there are more bars for incoming entropy, showing that Oceanus Folk was more often influenced by other genres than influencing others. This suggests that during the earlier years, Oceanus Folk was still developing its identity by absorbing influence from a diverse set of genres.

2013 to 2024: Influence Growth

Between 2013 and 2024, we observe a gradual and sustained rise in both incoming and outgoing entropy.This period can be seen as the maturation phase of Oceanus Folk, where it begins to both learn from and contribute to the wider music landscape.

2024 onwards: Sailor Shift’s Rise

Incoming entropy began to decline, suggesting that Oceanus Folk was no longer absorbing influence from other genres as it solidified its musical identity.

In contrast, outgoing entropy peaked and remained elevated, reflecting that other genres increasingly drew inspiration from Oceanus Folk. This sustained high outgoing influence signals that Oceanus Folk had become a genre of reference across many genres.

5.3.2 From which genres does Oceanus Folk draw most of its contemporary inspiration?

This analysis examines the contemporary influences on Oceanus Folk by using the genre influence patterns methodology similar to Section 5.2.1.

genre_influenced_by_stats <- creator_and_songs_and_influenced_by_creator %>%
  filter(song_genre == "Oceanus Folk") %>%
  distinct(song_to, influenced_by, influenced_by_genre) %>%
  group_by(influenced_by_genre) %>%
  summarize(
    total_music = n_distinct(song_to),
    influenced_by = n_distinct(na.omit(influenced_by))
  ) %>%
  arrange(desc(influenced_by))

genre_influenced_by_stats %>%
  head(10) %>%
  rename(
    `Genre` = influenced_by_genre,
    `Total Influenced Music` = total_music,
    `Influenced By Oceanus Folk` = influenced_by
  ) %>%
  kable(caption = "Ranking of Oceanus Folk Influence on Music Genres") %>%
  kable_styling("striped", full_width = F) %>%
  scroll_box(height = "200px")

Ranking of Oceanus Folk Influence on Music Genres

Genre	Total Influenced Music	Influenced By Oceanus Folk
Oceanus Folk	97	55
Indie Folk	93	22
Synthwave	16	17
Dream Pop	14	15
Doom Metal	14	13
Alternative Rock	8	8
Psychedelic Rock	9	8
Desert Rock	7	7
Indie Rock	8	7
Americana	5	5

library(networkD3)
library(htmlwidgets)

# Step 1: Prepare data — reverse flow direction: Genre → Oceanus Folk
sankey_df <- genre_influenced_by_stats %>%
  mutate(
    raw_source = influenced_by_genre,
    target = "Oceanus Folk",
    value = influenced_by,
    source = paste0(raw_source, " (", value, ")")
  ) %>%
  select(source, target, value) %>%
  arrange(desc(value)) %>%
  head(22)  # Top 22 genres influencing Oceanus Folk

# Step 2: Create node list
nodes <- data.frame(name = unique(c(sankey_df$source, sankey_df$target)))

# Step 3: Create link list with indices
links <- sankey_df %>%
  mutate(
    source = match(source, nodes$name) - 1,
    target = match(target, nodes$name) - 1
  )

# Step 4: Add tooltip group (Genre → Oceanus Folk)
links$group <- paste0(sankey_df$source, " → ", sankey_df$target, ": ", sankey_df$value)

# Step 5: Render Sankey
p <- sankeyNetwork(
  Links = links,
  Nodes = nodes,
  Source = "source",
  Target = "target",
  Value = "value",
  NodeID = "name",
  fontSize = 13,
  nodeWidth = 30,
  sinksRight = FALSE  # ← Flip layout: sources on right if needed
)

# Step 6: Add tooltips
onRender(p, '
  function(el, x) {
    d3.select(el)
      .selectAll(".link")
      .append("title")
      .text(function(d) { return d.group; });
  }
')

From which genres does Oceanus Folk draw most of its contemporary inspiration?

• As expected, Oceanus Folk artists primarily draw inspiration from other Oceanus Folk works, demonstrating strong genre continuity.
• Approximately a fifth of Oceanus Folk compositions appear to be original works without direct influences from existing pieces.
• Indie Folk emerges as the second most influential genre, accounting for roughly 21% of all Oceanus Folk musical references.

Reference

Figueiredo, F., Panoutsos, T., & Andrade, N. (2024, October 21). Surprising patterns in musical influence networks (Version 1) [Preprint]. arXiv. Retrieved from https://doi.org/10.48550/arXiv.2410.15996

Ferwerda, B., & Schedl, M. (2016). Investigating the relationship between diversity in music consumption behavior and cultural dimensions: A cross-country analysis (SOAP’16 workshop paper, UMAP Extended Proceedings). CEUR Workshop Proceedings, 1618. Retrieved from https://ceur-ws.org/Vol-1618/SOAP_paper1.pdf

6 VAST 2025 MC1 Question 3

Use your visualizations to develop a profile of what it means to be a rising star in the music industry.

6.1 Analysing the careers of all Artists

The analysis identifies all musical artists, including both the songs/albums they produced and their influence relationships—tracking both the musical works they have influenced and the artists they have impacted throughout their careers.

A grading rubric is added to assess artists based on four equally weighted categories:

1. Number of Music Releases
2. Number of Notable Music
3. Number of New Artists Influenced and Collaborated With
4. Number of Music Influenced

Each category’s score will be normalised using the formula:

library(latex2exp)
plot(1, 1, type = "n", ann = FALSE, axes = FALSE)
text(1, 1, 
     latex2exp::TeX("$\\frac{Artist's\\ Score - min((Score))}{max((Score)) - min((Score))}$"), 
     cex = 2)

The Star Factor will aggregate these normalized scores (maximum: 4) to rank the artists.

# Data Preparation
  
# Step 1: To highlight songs that the creator influence that is not produced by same creator
creator_influence_lists <- creator_and_songs_and_influences_and_creators_collaborate %>%
  group_by(creator_name, creator_node_type, song_to, song_name, creator_release_date, song_genre, notable) %>%
  distinct () %>%
  summarize(
    unique_collaborate = list(unique(na.omit(infuence_music_collaborate[creator_from != influence_creator & `Edge Colour` == "Creator Of"]))),
    unique_influence_creators = list(unique(na.omit(influence_creator[creator_from != influence_creator & `Edge Colour` == "Influenced By"]))),
    unique_influence_music = list(unique(na.omit(infuence_music_collaborate[creator_from != influence_creator & !is.na(influence_genre)])))
  )

# Step 2: Aggregate unique influences per creator
creator_stats <- creator_influence_lists %>%
  group_by(creator_name) %>%
  summarize(
    total_songs = n_distinct(song_to),
    notable_hits = sum(notable == TRUE, na.rm = TRUE),
    collaboration = length(unique(unlist(unique_collaborate))),
    influence_creators = length(unique(unlist(unique_influence_creators))),
    collaboration_influence_creator = length(unique(c(unlist(unique_influence_creators),unlist( unique_collaborate)))),
    influence_music = length(unique(unlist(unique_influence_music)))
  )

# Step 3: Create Scoring Rubric
creator_rankings <- creator_stats %>%
  mutate(
    # Normalize each metric (0-1 scale)
    songs_score = (total_songs - min(total_songs)) / (max(total_songs) - min(total_songs)),
    notable_score = (notable_hits - min(notable_hits)) / (max(notable_hits) - min(notable_hits)),
    artists_score = (collaboration_influence_creator - min(collaboration_influence_creator)) / (max(collaboration_influence_creator) - min(collaboration_influence_creator)),
    music_score = (influence_music - min(influence_music)) / (max(influence_music) - min(influence_music)),
    
    # Calculate composite score (equal weighting)
    composite_score = songs_score + notable_score + artists_score + music_score
  ) %>%
  arrange(desc(composite_score)) %>%
  select(creator_name, total_songs, notable_hits, collaboration_influence_creator, influence_music, composite_score)

# Step 4: Final Ranked Table
creator_rankings %>%
  mutate(
    Rank = row_number(),
    `Star Factor` = round(composite_score, 2)
  ) %>%
  select(Rank, creator_name, total_songs, notable_hits, collaboration_influence_creator, 
         influence_music, `Star Factor`) %>%
  rename(
    `Artist` = creator_name,
    `Total Music` = total_songs,
    `Notable Hits` = notable_hits,
    `Artist Influ & Colab` = collaboration_influence_creator,
    `Music Influenced` = influence_music
  ) %>%
  kable(caption = "Artists Ranked by Star Factor") %>%
  kable_styling("striped", full_width = F) %>%
  scroll_box(height = "300px")

Artists Ranked by Star Factor

Rank	Artist	Total Music	Notable Hits	Artist Influ & Colab	Music Influenced	Star Factor
1	Min Fu	12	12	598	162	2.64
2	Jay Walters	37	35	51	50	2.37
3	Jing Kang	29	28	176	70	2.28
4	Min Tao	6	6	588	157	2.26
5	Chao Tan	5	5	592	155	2.20
6	Ping Tian	32	31	115	42	2.18
7	Juan Gao	5	5	572	147	2.11
8	Qiang Yuan	9	9	511	125	2.10
9	Kimberly Snyder	29	27	153	49	2.09
10	Ping Meng	24	23	175	83	2.08
11	Yang Wan	24	23	274	56	2.08
12	Brett Brown	4	4	540	143	1.98
13	Angelika Osojca	28	26	95	55	1.97
14	Guiying Lu	3	3	554	142	1.94
15	Sailor Shift	38	25	48	0	1.79
16	Brittany Meyers	1	1	528	141	1.78
17	Corey Moody	1	1	528	141	1.78
18	Kristina Willis	1	1	528	141	1.78
19	Lisa Evans	1	1	528	141	1.78
20	Peter Huff	1	1	528	141	1.78
21	Susan Richardson	1	1	528	141	1.78
22	Thomas Kelly	1	1	528	141	1.78
23	Gang Chen	11	11	392	84	1.76
24	Ante Reinhardt-Hande	4	4	483	116	1.72
25	Jing Cui	22	22	193	26	1.68
26	Szymon Pyć	26	26	45	29	1.67
27	Guiying Cai	3	3	480	116	1.66
28	Yang Zhao	20	20	216	33	1.65
29	Tao Yang	20	19	184	40	1.61
30	Fang Zou	17	17	198	52	1.57
31	Megan Bennett	21	21	54	49	1.53
32	Andrew Williams	23	23	38	35	1.53
33	Maik Mude	19	19	77	53	1.49
34	Catherine Clay	22	21	52	34	1.46
35	Matthew Hancock	21	21	58	36	1.46
36	Na Ren	13	13	255	54	1.46
37	Qiang Tang	20	19	151	17	1.41
38	Min Lei	20	20	133	15	1.40
39	Yong Zheng	22	20	47	25	1.37
40	Bekir Bruder	14	14	187	46	1.35
41	Chao Wu	13	12	235	46	1.34
42	Lei Yuan	16	16	149	30	1.30
43	Yang Peng	15	14	138	43	1.27
44	Guiying Bai	19	18	64	25	1.26
45	Deborah Lucas	15	15	91	45	1.24
46	Urszula Stochmal	18	18	37	32	1.23
47	Xiuying Han	4	4	328	75	1.21
48	Xiuying Fan	12	12	192	37	1.19
49	Gerald Mullins	16	14	112	31	1.18
50	Brian Wright	15	15	50	46	1.17
51	Ming Huang	9	9	241	48	1.17
52	Ming Sun	10	10	134	66	1.16
53	Corinna Gute	11	11	180	44	1.16
54	Xia Wu	15	13	111	35	1.15
55	Jing Wang	13	13	147	32	1.14
56	Leyla Graf-Gotthard	17	16	87	16	1.13
57	Lei Duan	17	16	95	13	1.13
58	Min Kong	15	14	121	23	1.12
59	Quattro Voci	16	16	31	33	1.12
60	Guiying Liao	16	16	88	16	1.11
61	Li Lu	13	12	86	47	1.10
62	Jun Hao	11	11	175	36	1.10
63	Xiuying Yao	13	12	139	31	1.09
64	Gang Cui	7	7	228	55	1.08
65	Guiying Qin	9	9	198	45	1.08
66	Jing Tian	12	12	161	26	1.07
67	Xia Jia	9	8	199	45	1.06
68	Gang Zhao	16	16	49	16	1.04
69	Belinda Knappe	13	13	110	26	1.04
70	Regina Hesse	13	13	117	23	1.03
71	Wei Shen	7	7	219	49	1.03
72	Guiying Shi	15	15	76	15	1.03
73	Tao Gao	16	16	68	7	1.02
74	Joshua Herring	14	12	91	28	1.02
75	Xiuying Huang	14	14	91	18	1.01
76	Min He	13	13	119	18	1.01
77	Xiulan Ren	9	9	120	53	1.00
78	Daniël Reijers	14	13	46	32	1.00
79	Li Lei	11	11	136	30	1.00
80	Ping Zhong	12	12	127	23	0.99
81	Mareen Schacht	13	13	61	31	0.99
82	Yong Fu	12	12	123	21	0.98
83	Yan Tang	11	11	139	25	0.97
84	Yong Dong	14	14	85	12	0.97
85	Ming Jiang	8	8	192	37	0.97
86	Jie Yan	13	12	89	24	0.96
87	Xia Du	14	14	79	13	0.96
88	Jun Han	10	10	148	26	0.94
89	Lei Liao	12	11	76	32	0.94
90	Qiang Lei	8	8	96	57	0.93
91	Jun Dai	11	11	128	21	0.93
92	Ping He	13	13	91	13	0.93
93	Wei Liao	10	10	146	24	0.92
94	Juan Yin	12	11	112	19	0.92
95	Lei Yan	12	12	83	22	0.91
96	Juan Li	12	12	82	22	0.91
97	Xiuying Yin	12	12	105	15	0.91
98	Jun Yu	12	12	112	13	0.91
99	Marcus Spence	13	12	43	26	0.90
100	Ping Luo	14	14	56	8	0.89
101	Jie Lai	12	12	94	15	0.89
102	Gang Bai	13	12	81	14	0.89
103	Zacharie Martins	8	8	152	34	0.88
104	Sonic Renegade	12	10	82	26	0.88
105	Ping Bai	9	9	102	38	0.88
106	Jun Zhou	8	7	163	35	0.88
107	Shaan Sanghvi	13	12	55	19	0.88
108	Qiang Song	12	11	99	16	0.88
109	Yan Qin	13	13	63	12	0.88
110	Eugenia Mitschke	12	12	48	25	0.87
111	Chelsea Martinez	13	13	45	16	0.87
112	Jun Xia	11	11	102	18	0.87
113	Xia Zeng	12	12	90	12	0.86
114	Li Yang	12	12	92	11	0.86
115	Qiang Dong	6	6	186	39	0.86
116	Lei Xue	13	13	67	8	0.86
117	Yuta Tanaka	11	11	61	27	0.85
118	Juan Zhong	10	10	123	19	0.85
119	Arctic Scholars	9	8	73	46	0.85
120	Jun Yin	12	12	73	14	0.85
121	Lei Yi	11	11	113	12	0.85
122	Yan Zou	11	11	94	17	0.85
123	Guiying Kang	8	8	140	31	0.84
124	Lei Fu	10	10	121	18	0.84
125	Jun Mao	8	8	149	28	0.84
126	Chao Qiu	12	12	84	9	0.84
127	Ming Long	11	11	76	20	0.84
128	Li Zheng	12	11	63	18	0.83
129	Qiang Fang	9	9	122	24	0.83
130	Filippo Pelli	11	10	91	19	0.83
131	Guiying Pan	11	11	93	13	0.82
132	Li Luo	12	12	47	16	0.82
133	Juan Feng	9	7	134	27	0.81
134	Georges Besnard	11	11	44	24	0.81
135	Tao Sun	11	11	81	13	0.80
136	Jun Zou	11	10	50	25	0.79
137	Yan Feng	9	9	123	18	0.79
138	John Anderson	8	7	114	34	0.79
139	Alisha Fuller	5	5	169	41	0.79
140	Tao Dai	12	12	58	8	0.79
141	Peter Haering	11	11	70	13	0.78
142	Qiang Zhao	11	11	66	14	0.78
143	Fang Yin	12	12	43	9	0.77
144	Jun Fang	12	11	67	7	0.77
145	Kelly Stewart	10	8	79	26	0.76
146	Guiying Gao	11	11	59	13	0.76
147	Kimberly Castillo	11	9	71	19	0.76
148	Rachel Jackson	8	8	103	28	0.76
149	Yan Jin	10	9	68	24	0.76
150	Robin Webster	10	10	43	26	0.76
151	Jun Meng	9	9	107	17	0.76
152	Susan Edwards	3	3	194	46	0.75
153	Min Qin	11	11	65	8	0.74
154	Qiang Gao	9	9	98	16	0.74
155	Min Gao	7	7	146	21	0.74
156	José Berger	4	4	163	43	0.73
157	Adam Sanders	1	1	240	49	0.73
158	Christina Blair	1	1	240	49	0.73
159	Gabriela Campos	1	1	240	49	0.73
160	Noah Wilson	1	1	240	49	0.73
161	Tyler Brown	1	1	240	49	0.73
162	Min Zhao	9	9	99	15	0.73
163	Yang Pan	9	8	105	18	0.73
164	Jun Shao	8	8	105	22	0.73
165	Samantha Bullock	8	8	71	31	0.73
166	Tao Yao	10	10	82	10	0.73
167	Wei Ding	10	9	47	24	0.73
168	Ping Zeng	11	11	52	9	0.73
169	Min Cao	7	7	118	27	0.73
170	Chao Yao	7	7	92	34	0.73
171	Guiying Zhou	11	11	62	6	0.73
172	Gang Peng	6	6	139	30	0.72
173	Brüderklang	2	2	212	46	0.72
174	Tao Peng	10	9	73	16	0.72
175	Yan Liu	8	8	118	17	0.72
176	Navya Sastry	11	10	45	14	0.72
177	Jun Chang	7	7	118	25	0.71
178	Li Gu	10	10	64	12	0.71
179	Na Dong	10	10	69	10	0.71
180	Gang Shen	11	11	50	6	0.71
181	Lei Wang	11	11	33	10	0.70
182	Chao Zhu	7	7	110	25	0.70
183	Na Qian	9	9	80	15	0.70
184	Ming Duan	6	6	119	31	0.70
185	Ming Zhou	9	9	81	14	0.70
186	Gang Wan	6	6	136	26	0.69
187	Yan Kang	11	11	36	8	0.69
188	Li Song	7	6	130	23	0.69
189	Marilyn Moore	2	2	194	46	0.69
190	Qiang He	9	9	74	15	0.69
191	Shannon Gordon	9	7	81	21	0.68
192	Tao Ren	6	6	139	23	0.68
193	Yong Shen	7	6	115	25	0.68
194	Chao Zhong	10	10	56	9	0.68
195	Ping Liao	10	10	56	9	0.68
196	Li Jin	10	10	67	6	0.68
197	Yan Yan	10	10	63	7	0.68
198	Xia Dai	10	10	51	10	0.68
199	Yan Cai	10	10	51	10	0.68
200	Sandra Smith	3	2	179	43	0.68
201	Yong Wei	5	5	147	29	0.68
202	Xiulan Ye	3	2	180	42	0.67
203	Ming Xiang	9	9	74	12	0.67
204	Ming Chen	8	7	109	16	0.67
205	Gang Shao	7	7	108	20	0.67
206	Wei Zhao	8	8	67	22	0.67
207	Lupe Miró	9	9	77	10	0.66
208	Na Lai	9	9	77	10	0.66
209	Wayfinder's Lament	18	7	2	0	0.66
210	Tao Cui	9	9	80	9	0.66
211	Fang Su	9	9	75	10	0.66
212	Yan Liang	9	9	75	10	0.66
213	Xiuying Yuan	7	7	108	19	0.66
214	The Salty Wakes	21	4	3	0	0.66
215	Jie Cui	9	9	70	11	0.66
216	Theresa Rivera	8	8	92	14	0.66
217	Yang Cui	9	9	73	10	0.66
218	Ming Lei	5	5	134	29	0.65
219	Gianfranco Falier	8	8	71	19	0.65
220	Sean Jones	9	8	47	21	0.65
221	Gang Qin	9	9	62	12	0.65
222	Juan Fan	8	6	103	19	0.65
223	Lei Bai	8	8	83	15	0.65
224	Xiuying Xiang	9	9	64	11	0.65
225	Bradley Hayes	8	8	49	24	0.65
226	Dennis Lee	8	8	49	24	0.65
227	Rüdiger Graf	10	9	48	10	0.64
228	Daniel Lyons	10	10	41	7	0.64
229	Xiulan Yi	9	9	59	11	0.64
230	Xiulan Shen	9	9	66	9	0.64
231	Lei Tan	6	6	121	21	0.64
232	Jadwiga Gertz	7	7	91	20	0.64
233	Lei Jin	8	7	91	15	0.63
234	Guiying Yan	10	9	54	7	0.63
235	Wei Mo	10	10	43	5	0.63
236	Fang Tao	8	8	91	10	0.63
237	Li Xie	6	6	113	22	0.63
238	Xia Ren	9	9	56	10	0.63
239	Ewelina Jeszke	8	8	40	23	0.63
240	Ping Sun	9	9	54	10	0.63
241	Angelina Dallapé-Brunelleschi	10	10	17	11	0.63
242	Michelotto Comisso	10	10	17	11	0.63
243	Jie Tao	10	10	42	4	0.62
244	Ping Ding	5	5	123	27	0.62
245	Miki Hayashi	8	8	81	11	0.62
246	Fang Yan	3	3	161	34	0.62
247	Min Liao	8	8	83	10	0.62
248	Xiuying Zeng	6	6	119	18	0.62
249	Clémence Vincent	3	3	166	32	0.61
250	Laura Turner	7	7	73	21	0.61
251	Jun Jia	5	5	121	26	0.61
252	Min Jin	9	9	50	9	0.61
253	Chad Hampton	10	10	19	8	0.61
254	Brian Johnson	6	6	110	19	0.61
255	Emily Nelson	8	8	65	13	0.61
256	Xiulan Zeng	10	9	34	8	0.61
257	Guiying Dai	6	6	109	19	0.61
258	Alla Lorch	10	10	16	8	0.61
259	Sandro Gröttner	10	10	16	8	0.61
260	Qiang Lu	9	9	56	6	0.60
261	Vanessa Ramos	7	7	88	15	0.60
262	Cristian Bonilla	9	9	39	10	0.60
263	Juan Shi	8	8	72	10	0.60
264	Tao Ye	7	7	79	17	0.60
265	Ping Mo	8	8	75	9	0.60
266	Xia Cui	8	8	63	12	0.60
267	Ming Qiao	7	7	81	16	0.60
268	Xiulan Xue	9	9	58	4	0.60
269	Joseph Vargas	1	1	209	35	0.59
270	Keith Harrison	1	1	209	35	0.59
271	Donna Caldwell	2	1	170	41	0.59
272	Juan Jia	7	7	85	14	0.59
273	Na Zhou	10	9	27	7	0.59
274	Xiuying Ding	10	8	39	8	0.59
275	Yan Long	9	9	36	8	0.58
276	Vidur Kapadia	9	9	16	13	0.58
277	SideShift	6	6	97	18	0.58
278	Yang Ye	7	7	82	13	0.58
279	Min Qian	8	8	67	8	0.58
280	Cindy Lübs	7	7	74	15	0.58
281	Chao Zeng	7	7	77	14	0.58
282	Xiuying Song	8	8	58	10	0.58
283	Na Shao	8	8	69	7	0.58
284	Fang Xiong	4	4	128	27	0.58
285	Ida Hentschel	6	6	87	20	0.58
286	Gang Fan	7	7	90	10	0.57
287	Thibault Potier	7	7	53	20	0.57
288	Jie Fan	7	6	88	15	0.57
289	Min Deng	8	8	56	10	0.57
290	Radical Syntax	7	7	66	16	0.57
291	Jie Huang	4	4	121	28	0.57
292	Yan Shi	9	9	39	5	0.57
293	Wei Gao	7	7	72	14	0.57
294	Li Duan	4	4	120	28	0.57
295	Yang Chang	6	6	94	17	0.57
296	Yong Meng	5	5	94	26	0.57
297	Tao Xie	8	8	51	10	0.56
298	Michael Boyd	8	7	31	20	0.56
299	Lei Gong	7	7	69	14	0.56
300	Wei Xiong	8	8	65	6	0.56
301	Gang Wen	9	8	48	6	0.56
302	Cynthia Dixon	8	8	53	9	0.56
303	Chao Yi	7	7	81	10	0.56
304	Xiuying Tang	8	8	66	5	0.56
305	Jun Jiang	7	7	84	9	0.56
306	Min Duan	9	7	62	6	0.56
307	Yan Lin	8	8	57	7	0.56
308	Roy Tschentscher	7	7	57	16	0.56
309	Tao Lei	8	8	56	7	0.55
310	Yong Lin	8	6	82	9	0.55
311	Li Ma	8	8	51	8	0.55
312	Na Fan	8	8	51	8	0.55
313	Juan Pan	6	6	91	15	0.55
314	Nordic Kitchen Collective	7	7	65	13	0.55
315	Keith Larsen	3	3	124	33	0.55
316	Guiying Ren	8	8	52	7	0.55
317	Lucrezia Zamengo	9	9	15	8	0.55
318	Min Yu	6	4	112	18	0.55
319	Li Wan	8	8	59	5	0.55
320	Jun Cao	8	8	44	9	0.55
321	Xiuying Zhu	4	4	114	26	0.55
322	Bobby Tucker	2	2	162	31	0.55
323	Christopher Reese	2	2	162	31	0.55
324	Daniel Lee	2	2	162	31	0.55
325	Leon Hutchinson	2	2	162	31	0.55
326	Melanie Bautista	2	2	162	31	0.55
327	Rémy-Bertrand Bailly	2	2	162	31	0.55
328	Xiuying Dai	6	6	88	15	0.55
329	Li Lin	8	8	51	7	0.55
330	Ochre	9	9	16	7	0.54
331	Ekkehard Förster	9	9	12	8	0.54
332	Wei Yuan	7	7	60	13	0.54
333	Jun Ye	7	7	63	12	0.54
334	Xiulan Lu	9	9	37	1	0.54
335	Kashvi Dhillon	8	7	39	14	0.54
336	Tao Zhang	8	8	47	7	0.54
337	Juan Wu	8	8	50	6	0.54
338	Xiuying Li	7	7	68	10	0.54
339	Kathryn Golden	7	6	74	13	0.54
340	Fang Zhong	7	6	70	14	0.54
341	Fang Lin	6	6	82	15	0.54
342	Yang Tang	8	8	55	4	0.53
343	Xiuying Zhang	5	5	99	19	0.53
344	Jing Jin	9	7	44	7	0.53
345	Li Bai	6	6	91	12	0.53
346	Qiang Shen	2	2	146	33	0.53
347	Chao Tian	7	7	57	12	0.53
348	Zenith Horizon	6	6	49	23	0.53
349	Xiulan Gong	7	7	67	9	0.53
350	Xiuying Xie	5	5	89	21	0.53
351	Na Wen	7	7	63	10	0.53
352	Florian van Santen-Sanders	8	8	44	6	0.53
353	Na Peng	8	7	63	5	0.53
354	Ping Fu	3	3	127	28	0.52
355	Chao Wan	8	8	38	7	0.52
356	Tao Xu	6	6	86	12	0.52
357	Xiulan Tao	7	7	60	10	0.52
358	Yan Yin	5	5	56	29	0.52
359	Xiulan Zhao	8	8	44	5	0.52
360	Yan Tian	6	6	77	14	0.52
361	Jun Lin	5	5	88	20	0.52
362	Xia Li	8	8	51	3	0.52
363	Thomas Weber	4	4	88	29	0.52
364	David Wilcox	8	7	26	14	0.52
365	John Ferguson	3	3	116	30	0.52
366	Ismet Heser	7	6	22	24	0.52
367	Ming Ding	8	8	45	4	0.52
368	Xiulan Liao	5	5	92	18	0.52
369	Jie Zheng	8	8	40	5	0.52
370	Yan Xiong	6	6	84	11	0.51
371	Przemysław Grupa	4	4	58	36	0.51
372	Na Jia	6	6	80	12	0.51
373	Ming Xia	3	3	128	26	0.51
374	Yan Wen	8	8	35	6	0.51
375	Qiang Lai	6	6	46	21	0.51
376	Aimée van Dijk	7	7	49	11	0.51
377	Hans Josef Haering	2	2	134	33	0.51
378	Juan Fu	7	7	56	9	0.51
379	Min Long	7	7	59	8	0.51
380	Na Pan	7	7	59	8	0.51
381	Christopher Smith	8	8	22	9	0.51
382	Na Zhong	8	8	33	6	0.51
383	Adam Davis	5	5	40	31	0.51
384	Yong Lei	7	7	62	7	0.51
385	Constance Guibert	3	3	117	28	0.51
386	Ignazio Pastine	3	3	117	28	0.51
387	Tao Zeng	7	7	54	9	0.51
388	Guiying Song	4	4	101	23	0.51
389	Jun Zhang	6	6	71	13	0.51
390	Tonya Brown	6	6	57	16	0.50
391	Wei Wan	7	6	69	8	0.50
392	Allison Patterson	5	4	80	23	0.50
393	Yan Xue	5	5	85	17	0.50
394	Samuel Rogers	6	6	69	12	0.50
395	Jun Xu	3	3	113	27	0.50
396	Tao Jin	7	7	50	8	0.50
397	Fang Wu	6	6	72	11	0.49
398	Xiulan Sun	8	8	38	2	0.49
399	Fang Bai	6	5	44	23	0.49
400	Yang Zhang	6	6	71	11	0.49
401	Xiuying Cheng	7	7	45	9	0.49
402	Andrew Green	1	1	162	31	0.49
403	Eric Bailey	1	1	162	31	0.49
404	Inès Lebreton	1	1	162	31	0.49
405	Lei Zhou	7	7	51	7	0.49
406	Jana Lindner	8	8	14	8	0.49
407	Juan Qiao	6	6	43	18	0.49
408	Li Liao	7	6	56	10	0.49
409	Qiang Shi	7	7	61	4	0.49
410	Xiulan Li	6	6	68	11	0.49
411	Qiang Bai	8	8	31	3	0.49
412	Xiulan Shi	7	7	31	12	0.49
413	Isabella Farinelli	6	5	73	14	0.49
414	Xiulan Liang	7	7	48	7	0.49
415	Yan Han	5	5	76	17	0.48
416	Min Hu	6	6	61	12	0.48
417	Yong Tao	6	6	72	9	0.48
418	Burrowing Rodent	5	5	68	19	0.48
419	Michelangelo Nolcini	5	5	68	19	0.48
420	Rita Iannucci	5	5	68	19	0.48
421	Jing Zhang	8	7	37	5	0.48
422	Min Jia	8	7	37	5	0.48
423	Xiulan Yao	6	6	64	11	0.48
424	Jun Wan	8	8	27	3	0.48
425	Yan Su	7	7	49	6	0.48
426	Chao Qiao	6	6	60	12	0.48
427	Guiying Ding	5	5	89	13	0.48
428	Na Deng	8	7	39	4	0.48
429	Lei Shen	6	6	57	12	0.48
430	Gang He	8	8	31	1	0.48
431	Karen Wright	7	7	27	11	0.48
432	Li Long	6	6	52	13	0.47
433	Jie Jiang	8	7	43	2	0.47
434	Jun Qiao	7	7	48	5	0.47
435	Jun Liang	4	4	99	18	0.47
436	Tao Liao	6	6	69	8	0.47
437	Xiuying Dong	7	7	43	6	0.47
438	Jing Pan	6	5	71	12	0.47
439	Nathan Bryant	5	5	76	15	0.47
440	Xia Lin	8	7	32	4	0.47
441	Yong Yao	7	7	37	7	0.47
442	Xiulan Qin	7	7	48	4	0.47
443	Tao Bai	7	6	54	7	0.47
444	Yong Wen	6	6	70	7	0.47
445	Na Ma	6	6	55	11	0.47
446	Dora Säuberlich	1	1	140	33	0.47
447	Jun Duan	5	5	77	14	0.47
448	Jonathan Poole	7	7	25	10	0.47
449	Interval Shift	7	7	36	7	0.47
450	Martino Michelangeli	7	7	36	7	0.47
451	Jing Guo	7	7	47	4	0.47
452	Xiulan Hou	6	6	56	10	0.46
453	Ping Xia	7	7	30	8	0.46
454	John Ramirez	4	3	58	32	0.46
455	Li Gong	8	8	26	0	0.46
456	Jie Shao	4	4	92	18	0.46
457	Erdogan Baum	5	5	73	14	0.46
458	Gang Zheng	5	5	73	14	0.46
459	Borys Kościk	8	8	10	4	0.46
460	Brandon Bartlett	5	5	69	15	0.46
461	Lunar Syntax	5	5	69	15	0.46
462	Na Guo	7	7	43	4	0.46
463	Jun Yi	6	6	54	10	0.46
464	The Wandering Pulse	7	7	17	11	0.46
465	Qiang Yang	6	6	65	7	0.46
466	Jie Wen	5	5	65	16	0.46
467	Tarini Hans	7	6	34	11	0.46
468	Ping Cai	5	5	76	13	0.46
469	Ping Pan	4	4	87	19	0.46
470	Wei Cai	4	4	94	17	0.46
471	Fang Duan	6	6	57	9	0.46
472	Lei Qiu	6	5	63	12	0.46
473	Qiang Cui	7	7	42	4	0.46
474	Xiulan He	6	6	53	10	0.46
475	Nordic Echo	6	5	33	20	0.46
476	Min Ye	6	6	37	14	0.45
477	Lei Hu	6	6	59	8	0.45
478	Juan Du	4	4	81	20	0.45
479	Katrin Steckel	1	1	132	33	0.45
480	Roswita Hande	1	1	132	33	0.45
481	Kaitlyn Warren	5	5	39	22	0.45
482	Linda Reed	8	7	15	6	0.45
483	Ming Cui	6	6	64	6	0.45
484	Guiying Hou	7	7	38	4	0.45
485	Gang Yuan	7	7	34	5	0.45
486	Min Xiang	6	6	67	5	0.45
487	Xia Xiong	6	6	55	8	0.45
488	Guiying Zeng	7	7	40	3	0.45
489	Drowned Harbor	12	5	4	0	0.45
490	Liliana Heidrich	4	4	106	12	0.45
491	Xiuying Meng	7	5	55	8	0.45
492	Wei Tang	8	7	19	4	0.45
493	Laetitia Bertrand-Becker	7	7	24	7	0.45
494	Jun Guo	6	6	57	7	0.45
495	Gang Yang	7	6	48	5	0.44
496	Zachary Harris	7	6	33	9	0.44
497	Yong Su	7	6	44	6	0.44
498	Jie Xu	6	5	77	6	0.44
499	Tao Yin	8	7	25	2	0.44
500	Lei Tang	8	6	34	4	0.44
501	Juan Duan	5	5	70	12	0.44
502	Ming Kang	4	4	70	21	0.44
503	Juan Kang	7	7	33	4	0.44
504	Juan Yao	7	6	50	4	0.44
505	Mia Carsten	5	5	66	13	0.44
506	Nathan Perez	5	5	69	12	0.44
507	Bryan Smith	6	6	50	8	0.44
508	Xiuying Kong	4	4	109	10	0.44
509	Lei He	7	7	35	3	0.44
510	Ping Lai	3	3	105	20	0.44
511	Na Cui	5	4	74	15	0.44
512	Yang Yu	7	7	31	4	0.44
513	Min Li	7	6	48	4	0.44
514	Na Su	5	5	75	10	0.44
515	Min Mo	7	6	36	7	0.44
516	Juan Yu	6	5	58	10	0.44
517	Lei Wan	3	3	100	21	0.44
518	Ping Jia	5	5	66	12	0.44
519	Ming Ren	6	6	51	7	0.44
520	Xiulan Wei	6	6	51	7	0.44
521	Xia Ding	5	5	62	13	0.43
522	Wei Luo	7	7	36	2	0.43
523	Qiang Dai	6	6	47	8	0.43
524	Mike Robinson	4	4	80	17	0.43
525	Lei Luo	6	6	54	6	0.43
526	Dwayne Wilson	7	7	28	4	0.43
527	Wei Shi	6	6	50	7	0.43
528	Barbara Weitzel	4	4	61	22	0.43
529	Li Deng	7	6	41	5	0.43
530	Brandi Rodriguez	5	5	35	20	0.43
531	Na Lu	3	3	105	19	0.43
532	Ping Song	7	7	31	3	0.43
533	Xia Chen	7	7	31	3	0.43
534	Xia Xue	6	6	42	9	0.43
535	Tao Su	6	6	53	6	0.43
536	Silvio Krause	7	7	16	7	0.43
537	Gang Tan	7	7	34	2	0.43
538	Jing Long	6	6	52	6	0.43
539	Jing Lin	5	5	63	12	0.43
540	Xiulan Lin	6	6	48	7	0.43
541	Tao Hu	6	6	44	8	0.43
542	Xiuying Ye	6	6	55	5	0.43
543	Angelina Sölzer	5	5	55	14	0.43
544	Denise Bell	5	5	40	18	0.43
545	Janice Lamb	6	6	25	13	0.43
546	Jie Su	7	7	36	1	0.43
547	Xia Zhu	6	6	47	7	0.43
548	Xiulan Dong	6	6	47	7	0.43
549	Yong Wang	6	6	43	8	0.43
550	Xiulan Yan	6	6	46	7	0.43
551	Nada Pechel	4	4	68	19	0.43
552	Ping Hao	7	7	27	3	0.43
553	David Wilkerson	8	7	7	4	0.43
554	Audrey-Camille Nicolas	8	7	10	3	0.42
555	Fang Cui	5	4	69	14	0.42
556	Gang Pan	7	7	22	4	0.42
557	Christine Gonzales	6	6	44	7	0.42
558	Fang Kong	6	6	44	7	0.42
559	Wei Du	4	4	55	22	0.42
560	Juan Zou	6	6	47	6	0.42
561	Chao Yu	6	6	58	3	0.42
562	Xiuying Fang	7	6	38	4	0.42
563	Gang Yin	6	6	43	7	0.42
564	Starseed	5	5	54	13	0.42
565	Yan Qiu	7	6	45	2	0.42
566	Juan Xiong	4	4	83	14	0.42
567	Lorraine Martinez	7	7	20	4	0.42
568	Chao Xiao	6	6	42	7	0.42
569	Guiying Shao	5	5	64	10	0.42
570	Gino Michelangeli	6	6	38	8	0.42
571	Midnight Protocol	2	2	108	25	0.42
572	Lei Yang	6	6	41	7	0.42
573	Orla Seabloom	8	8	0	0	0.42
574	Yong Yan	6	6	40	7	0.42
575	Li Zeng	6	6	43	6	0.42
576	Ping Xiang	6	6	39	7	0.41
577	Clayton Reed	6	6	24	11	0.41
578	Chao Zhao	6	6	46	5	0.41
579	Fang Mao	7	7	20	3	0.41
580	Ming Yao	7	7	31	0	0.41
581	Qiang Gu	6	6	42	6	0.41
582	Fang Zhou	6	5	55	7	0.41
583	Juan Jin	5	5	60	10	0.41
584	Ivy Echos	11	4	13	1	0.41
585	Donald Choi	5	5	48	13	0.41
586	Guiying Huang	4	4	77	14	0.41
587	Yong Jia	6	6	51	3	0.41
588	Jun Ding	4	4	62	18	0.41
589	Melanie Jimenez	7	7	10	5	0.41
590	Jun Xie	6	6	39	6	0.41
591	Ping Qin	6	6	39	6	0.41
592	Jing Lai	5	5	57	10	0.41
593	Tao Hou	5	5	57	10	0.41
594	Yang He	5	5	68	7	0.41
595	Chao Wei	6	6	38	6	0.41
596	Jie Zhang	6	6	38	6	0.41
597	Kayla Scott	3	3	86	20	0.41
598	Leo Eberth	6	5	51	7	0.41
599	Robert Knight	5	5	56	10	0.41
600	Fang Lu	5	5	67	7	0.41
601	Yan Zhou	7	6	35	2	0.40
602	Gang Cao	5	5	51	11	0.40
603	David Armstrong	6	6	36	6	0.40
604	Xiulan Mo	6	6	43	4	0.40
605	Patricia Humphrey	2	1	115	25	0.40
606	Vera Rivero Palomo	2	2	94	26	0.40
607	Yolanda Barroso Pineda	2	2	94	26	0.40
608	Kelsey Campbell	6	6	27	8	0.40
609	Jie Zhu	6	6	38	5	0.40
610	Fang Chen	6	6	34	6	0.40
611	Na Yang	5	5	56	9	0.40
612	Xiulan Liu	4	4	78	12	0.40
613	Wei Han	6	5	47	7	0.40
614	Xiuying Cai	5	3	75	13	0.40
615	Tao Fang	6	6	37	5	0.40
616	Matthew Griffin	5	5	48	11	0.40
617	Min Wen	5	5	59	8	0.40
618	Wei Wang	5	5	59	8	0.40
619	Yang Gu	6	6	44	3	0.40
620	Ping Li	5	5	62	7	0.40
621	Guglielmo Canetta	6	6	25	8	0.40
622	Nicola Bernetti	6	6	25	8	0.40
623	Serena Lettiere	6	6	25	8	0.40
624	Vincentio Gentili	6	6	25	8	0.40
625	Xiuying Deng	6	6	36	5	0.40
626	Na Mao	3	3	91	17	0.40
627	Wei Su	6	6	39	4	0.40
628	Xia Xia	5	5	50	10	0.40
629	The Fiddle & The Fjord	12	3	4	1	0.40
630	Xiuying Qiu	6	6	42	3	0.40
631	Xia Fu	7	7	16	1	0.40
632	Francesca Maglio	6	6	30	6	0.39
633	Qiang Chang	5	5	63	6	0.39
634	Rei Ito	4	4	74	12	0.39
635	Min Hou	6	6	33	5	0.39
636	Tao Wei	6	6	33	5	0.39
637	Ping Yao	5	5	33	14	0.39
638	Gang Long	4	4	66	14	0.39
639	Xia Yuan	5	4	68	9	0.39
640	Joseph Dixon	3	3	106	12	0.39
641	Radiation Pulse	3	3	106	12	0.39
642	Ming Yan	4	4	69	13	0.39
643	Jing Ren	6	6	43	2	0.39
644	Jie Cai	6	6	35	4	0.39
645	Monica Brown	5	5	57	7	0.39
646	Jing Chang	5	5	53	8	0.39
647	Fang Liang	6	6	38	3	0.39
648	Lei Kong	6	6	34	4	0.39
649	Qiang Tan	3	3	93	15	0.39
650	Gang Dong	4	3	73	16	0.39
651	Jing Sun	5	5	52	8	0.39
652	Jing Mao	5	4	58	11	0.39
653	Xiuying Bai	6	6	37	3	0.39
654	Ping Wu	6	6	33	4	0.39
655	Arnaude Joseph-Lacombe	6	5	39	7	0.39
656	Guiying Tian	5	5	55	7	0.39
657	Ming Mao	5	5	55	7	0.39
658	Juan Xu	5	4	61	10	0.39
659	Yan Tan	6	6	29	5	0.39
660	Jing Qian	5	5	51	8	0.39
661	Yang Liao	5	5	51	8	0.39
662	Ashley Cox PhD	4	4	69	12	0.38
663	Blueprints	4	4	69	12	0.38
664	Primal Rhythm	4	4	69	12	0.38
665	Sara Parker	5	5	54	7	0.38
666	Jie Fang	5	5	57	6	0.38
667	Juan Mo	5	4	48	13	0.38
668	Chao Yang	5	5	53	7	0.38
669	Ming Yang	6	6	30	4	0.38
670	Helen Simon	5	5	30	13	0.38
671	Luminous Fracture	3	3	78	18	0.38
672	Robert Garcia	3	3	78	18	0.38
673	Wei Cheng	5	5	41	10	0.38
674	Jing Tan	6	6	33	3	0.38
675	Qiang Xie	5	5	55	6	0.38
676	Jie Cao	7	6	24	1	0.38
677	Ming Zhao	6	4	52	7	0.38
678	Monica Nelson	5	5	51	7	0.38
679	Heidi Jones	4	4	62	13	0.38
680	Min Shi	6	6	36	2	0.38
681	Xia Liang	6	6	32	3	0.38
682	Chao Li	6	4	44	9	0.38
683	Harro Trapp-Caspar	5	5	28	13	0.38
684	Gang Zhang	5	5	50	7	0.38
685	Ming Cheng	6	5	41	5	0.38
686	Yang Bai	5	5	46	8	0.38
687	Ping Yan	3	2	100	16	0.38
688	Na Kong	5	5	57	5	0.38
689	Juan Dong	6	6	31	3	0.38
690	Lei Zheng	6	6	31	3	0.38
691	Andrew Dawson	4	4	42	18	0.38
692	Xiulan Du	6	6	38	1	0.38
693	Juan Ding	6	6	34	2	0.38
694	Lei Long	5	5	45	8	0.38
695	Caroline Reilly	3	2	99	16	0.38
696	Lori Waters	3	2	99	16	0.38
697	Necropolis Noir	3	2	99	16	0.38
698	Jasmine Adams	4	4	67	11	0.38
699	Guiying Shen	5	5	48	7	0.37
700	Karl-August Beyer	4	4	55	14	0.37
701	Kelsey Hernandez	6	6	29	3	0.37
702	Yong Lu	5	5	40	9	0.37
703	Min Qiu	5	5	51	6	0.37
704	Jie Long	6	6	25	4	0.37
705	Qiang Zhang	6	6	25	4	0.37
706	Na Zou	5	5	47	7	0.37
707	Anthony Ortega	3	3	58	22	0.37
708	Lei Zhao	6	6	32	2	0.37
709	Erica Walton	5	4	34	15	0.37
710	Qiang Wu	5	5	50	6	0.37
711	Tobias Etzold-Dörr	5	5	50	6	0.37
712	Kristin Anderson	4	4	61	12	0.37
713	Rachel Hill	5	5	42	8	0.37
714	Li Yin	5	5	53	5	0.37
715	Denise White	4	4	53	14	0.37
716	Twilight Riders	4	4	53	14	0.37
717	Chao Han	5	5	49	6	0.37
718	Chao Xu	5	5	49	6	0.37
719	Min Su	6	6	19	5	0.37
720	Jing Qin	4	4	67	10	0.37
721	Wei Gu	6	6	30	2	0.37
722	Wei Feng	1	1	111	25	0.37
723	Ping Shen	3	3	74	17	0.37
724	Yang Wu	6	4	49	6	0.37
725	Kavya Agrawal	3	3	37	27	0.37
726	Cemil Linke	2	2	85	23	0.37
727	Fedor Bolzmann	2	2	85	23	0.37
728	Embers of Wrath	1	1	107	26	0.37
729	Anaïs Wagner	3	3	81	15	0.37
730	Neue Schicht	3	3	81	15	0.37
731	Yong Lai	5	4	50	10	0.37
732	Beatrice Albright	7	7	3	0	0.37
733	Daniel O'Connell	7	7	3	0	0.37
734	Ryan Doyle	6	6	14	6	0.37
735	The Forgotten Archives	2	2	62	29	0.37
736	Min Ren	4	3	79	11	0.37
737	Li Xue	4	4	65	10	0.37
738	Jie Zou	6	5	34	5	0.37
739	Copper Canyon Ghosts	7	7	2	0	0.37
740	Juan Cao	5	5	50	5	0.37
741	Min Huang	5	5	50	5	0.37
742	Juan Yang	4	4	61	11	0.37
743	Guiying Yao	6	6	24	3	0.37
744	Jing Zhong	6	6	24	3	0.37
745	Chao Peng	6	5	37	4	0.36
746	Guiying Cao	5	5	42	7	0.36
747	Chao Chen	6	6	23	3	0.36
748	Jing Yi	5	5	41	7	0.36
749	Jun Jin	4	4	63	10	0.36
750	Margarita Henschel	5	5	33	9	0.36
751	Fredo Grossi	5	5	44	6	0.36
752	Yasmin Verschut	6	6	7	7	0.36
753	Xia Cai	5	5	40	7	0.36
754	Li He	6	6	25	2	0.36
755	Julia Carter	8	6	0	0	0.36
756	Kristi Crane	6	5	16	9	0.36
757	Jun Song	4	4	69	8	0.36
758	Lei Feng	3	3	69	17	0.36
759	Yan Duan	5	5	43	6	0.36
760	Li Han	3	3	65	18	0.36
761	Gang Lei	5	5	39	7	0.36
762	Jun Pan	5	5	39	7	0.36
763	Xiuying Mo	5	5	39	7	0.36
764	Qiang Qian	6	5	37	3	0.36
765	Yang Hou	6	6	27	1	0.36
766	Jie Ding	5	5	38	7	0.36
767	Jakub Dziuda	6	6	12	5	0.36
768	Yong Cao	5	5	45	5	0.36
769	Yan He	5	5	41	6	0.36
770	Angelita Belda Enríquez	5	5	37	7	0.36
771	Gang Du	6	5	28	5	0.36
772	Cheryl Carter	5	5	33	8	0.36
773	Min Xiong	5	5	44	5	0.36
774	Min Sun	5	5	40	6	0.35
775	Gang Jia	5	5	51	3	0.35
776	Graziella Saffi	6	6	14	4	0.35
777	Yang Zhong	5	5	47	4	0.35
778	Gang Mo	6	5	16	8	0.35
779	Susan Anderson	3	3	69	16	0.35
780	Ming Zheng	5	5	43	5	0.35
781	Juan Song	6	6	28	0	0.35
782	Charles Vazquez	5	5	39	6	0.35
783	Liwia Olbrych	6	5	19	7	0.35
784	Fang Xiang	6	6	24	1	0.35
785	Tao Wen	5	5	35	7	0.35
786	Jeffery Bailey	2	2	83	21	0.35
787	Ping Zhou	6	6	20	2	0.35
788	Bartek Krężołek	6	6	16	3	0.35
789	Martine Normand	6	6	12	4	0.35
790	Li Dong	4	4	60	9	0.35
791	Jie Hu	5	5	30	8	0.35
792	Vito Stroh	5	5	30	8	0.35
793	Yan Zhu	5	5	33	7	0.35
794	Jivika Tank	5	5	14	12	0.35
795	Qiang Han	6	6	21	1	0.35
796	Laura Sanchez	6	6	6	5	0.35
797	Na Dai	6	5	34	2	0.35
798	Karina Hess	5	5	35	6	0.35
799	Yang Yao	4	3	63	12	0.35
800	Dominic Riehl	4	3	33	20	0.35
801	Jun Luo	5	5	38	5	0.35
802	Lei Cao	6	6	19	1	0.34
803	Yong Chen	4	4	52	10	0.34
804	Lisa Martin	3	3	74	13	0.34
805	Albert Miller	5	5	37	5	0.34
806	Gang Sun	5	4	43	8	0.34
807	Chao Fan	6	5	35	1	0.34
808	Yan Lu	5	5	36	5	0.34
809	Michelle Davis	5	5	32	6	0.34
810	Juan Sun	4	4	54	9	0.34
811	Christopher Lee	5	5	28	7	0.34
812	Ping Wan	5	5	39	4	0.34
813	Min Liang	5	5	35	5	0.34
814	Yong Zeng	5	5	35	5	0.34
815	Gang Zhou	5	5	38	4	0.34
816	Min Shao	5	5	38	4	0.34
817	Xia Deng	4	4	38	13	0.34
818	Yong Gao	3	3	71	13	0.34
819	Na Wei	5	5	34	5	0.34
820	Ming Xiong	4	4	52	9	0.34
821	Na Gu	4	4	63	6	0.34
822	Juan Xiao	6	5	32	1	0.34
823	Sarah Johnson	3	3	74	12	0.34
824	Xiulan Wang	5	4	43	7	0.34
825	The Saltwater Weavers	9	4	4	0	0.34
826	Xiulan Hao	6	5	28	2	0.34
827	Ping Jiang	5	5	33	5	0.34
828	Lisa Jones	3	3	66	14	0.34
829	Ann Holland	1	1	99	23	0.34
830	Eric Beasley	1	1	99	23	0.34
831	Juan Wei	5	5	36	4	0.34
832	Klaus-Günter Kensy	4	4	43	11	0.34
833	Na Chen	4	4	50	9	0.33
834	Xiulan Yuan	4	4	50	9	0.33
835	Franck Hernandez Le Leclerc	6	5	19	4	0.33
836	Christopher Johnson	2	2	109	11	0.33
837	Fang Meng	5	5	35	4	0.33
838	Qiang Pan	5	5	35	4	0.33
839	Tao Feng	5	5	35	4	0.33
840	Xia Zhou	5	5	35	4	0.33
841	Xiulan Cao	5	5	35	4	0.33
842	Xiulan Lai	5	5	35	4	0.33
843	Guiying Jia	4	4	57	7	0.33
844	Juan Gong	6	6	16	0	0.33
845	Wei Liu	6	6	12	1	0.33
846	Asta Reising	1	1	93	24	0.33
847	Karin Metz	1	1	93	24	0.33
848	Renegade Harmony	3	3	67	13	0.33
849	Claire Holmes	8	5	0	0	0.33
850	Meike van Boven	4	4	52	8	0.33
851	Na Yi	6	5	21	3	0.33
852	Yong Tian	3	3	63	14	0.33
853	Fang Tang	5	5	37	3	0.33
854	Jing Gao	3	3	70	12	0.33
855	Yan Fang	5	5	33	4	0.33
856	Lei Ma	6	4	30	5	0.33
857	Li Cheng	5	5	36	3	0.33
858	Kelli Rodriguez	3	3	69	12	0.33
859	Li Jia	5	5	32	4	0.33
860	Ping Fan	5	5	32	4	0.33
861	Ping Xiong	2	2	80	18	0.33
862	Tao Han	2	2	80	18	0.33
863	Na Xiao	4	4	54	7	0.33
864	Tao Dong	3	3	65	13	0.33
865	Guiying Zhong	5	5	39	2	0.33
866	Juan Liu	5	5	39	2	0.33
867	Na Yin	5	5	39	2	0.33
868	Jie Wei	4	4	46	9	0.33
869	Iolanda Prada	5	5	31	4	0.33
870	Jie Fu	5	5	31	4	0.33
871	Na Li	5	5	31	4	0.33
872	Wei Xiao	5	5	27	5	0.33
873	Min Jiang	5	5	38	2	0.33
874	Yan Luo	5	5	38	2	0.33
875	Qiang Ren	3	3	71	11	0.33
876	Yong Yi	5	5	34	3	0.33
877	Guiying Lei	5	5	30	4	0.33
878	Xia Kang	5	5	37	2	0.33
879	Dunja Reinhardt	5	5	22	6	0.32
880	Tao Kang	4	4	55	6	0.32
881	Catherine Prévost	5	5	18	7	0.32
882	Lilo Cichorius	5	5	14	8	0.32
883	Tina Hobbs	3	3	62	13	0.32
884	Jun Wei	5	5	32	3	0.32
885	Qiang Xu	5	5	32	3	0.32
886	Wei Yao	5	5	32	3	0.32
887	Xia Qin	5	5	32	3	0.32
888	Jing Yang	3	3	65	12	0.32
889	Feliciana Figuerola Cruz	5	5	28	4	0.32
890	Ming Fang	3	3	72	10	0.32
891	Rusty Riggins	6	6	9	0	0.32
892	Qiang Zhou	5	5	20	6	0.32
893	Tao Guo	5	5	31	3	0.32
894	Tao Shen	5	5	31	3	0.32
895	Adrien Charles	5	4	37	6	0.32
896	Olga Labudda	4	4	31	12	0.32
897	Yong Zhu	5	5	27	4	0.32
898	Fang Du	5	5	34	2	0.32
899	Xiuying Peng	5	5	34	2	0.32
900	Janet Wood	2	1	99	16	0.32
901	Olivia Morgan	2	1	99	16	0.32
902	Ricky Evans	2	1	99	16	0.32
903	Theresa Bradley	2	1	99	16	0.32
904	The Tide Singer's Knot	8	4	6	1	0.32
905	Xiulan Zheng	5	5	30	3	0.32
906	Tiffany Gill	4	4	30	12	0.32
907	Juan Wen	5	5	26	4	0.32
908	Ping Xu	5	5	33	2	0.32
909	Gang Yu	5	4	39	5	0.32
910	Qiang Fan	5	4	39	5	0.32
911	Liquid Vortex	3	3	55	14	0.32
912	Yong Tan	4	4	40	9	0.32
913	Luis Holt	2	2	99	11	0.32
914	Silke Verda	2	2	99	11	0.32
915	Michelle Williams	4	4	47	7	0.32
916	Min Xie	4	4	47	7	0.32
917	Laurie Campbell	4	4	17	15	0.32
918	Xavier Leclerc	5	5	28	3	0.32
919	Fang Ding	5	4	30	7	0.32
920	Li Xia	5	5	31	2	0.32
921	Ryan Rodriguez	4	4	53	5	0.31
922	Marcia Davis	5	4	22	9	0.31
923	Carolyn Schultz	6	5	7	4	0.31
924	Lori Hall	5	5	12	7	0.31
925	Chao Yan	5	5	23	4	0.31
926	Lei Fan	5	5	30	2	0.31
927	Brian Gonzalez	5	5	26	3	0.31
928	Noah Overeem	5	5	26	3	0.31
929	Ryan Austin	2	2	74	17	0.31
930	Whispers & Echoes	4	4	48	6	0.31
931	Annegrete Röhrdanz	4	3	54	9	0.31
932	Juan Long	5	5	22	4	0.31
933	Na Yao	5	5	22	4	0.31
934	Yang Zheng	5	4	39	4	0.31
935	Li Qiu	2	2	81	15	0.31
936	Joshua Cruz	4	4	44	7	0.31
937	Pulse Collective	4	4	44	7	0.31
938	Li Gao	5	5	29	2	0.31
939	Min Chen	5	5	29	2	0.31
940	Guiying Tao	5	4	35	5	0.31
941	The Hollow Monarchs	7	5	4	0	0.31
942	Cassian Storm	8	4	1	1	0.31
943	Wei Cui	5	5	25	3	0.31
944	Yesenia Miller	1	1	95	20	0.31
945	Hinrich Weimer	3	3	58	12	0.31
946	Jun Xiong	5	5	21	4	0.31
947	Ming Xue	4	4	43	7	0.31
948	Guiying Yuan	5	5	28	2	0.31
949	Angelica Sullivan	2	2	98	10	0.31
950	Kay van Dooren-Bastiaense	2	2	98	10	0.31
951	Gerhild Mälzer-Albers	1	1	83	23	0.31
952	Hannelore Rosemann	1	1	83	23	0.31
953	Lei Cui	4	4	46	6	0.31
954	Jeanne Guillou	5	5	16	5	0.31
955	Sienna Fox	7	5	2	0	0.31
956	Min Guo	4	4	49	5	0.31
957	Travis Williams	5	4	29	6	0.31
958	Jie Deng	5	5	30	1	0.31
959	Bernfried Stolze	4	4	41	7	0.31
960	Min Xia	4	3	58	7	0.31
961	Jason Jordan	3	3	52	13	0.31
962	Yang Xu	2	2	81	14	0.31
963	Lei Qin	4	4	44	6	0.31
964	Yong Sun	4	4	44	6	0.31
965	David Lopez	4	4	40	7	0.31
966	Joseph Alvarado	3	3	62	10	0.31
967	Min Cui	5	5	25	2	0.31
968	Ming He	5	5	25	2	0.31
969	Yan Yuan	5	5	25	2	0.31
970	Willow Sage	7	5	0	0	0.31
971	Laura Dillon	1	1	80	23	0.30
972	Nicholas Boyd	1	1	80	23	0.30
973	Wei Cao	4	4	43	6	0.30
974	Gundula Löchel	3	3	54	12	0.30
975	Phantom Harmony	3	3	54	12	0.30
976	Qiang Du	5	5	28	1	0.30
977	Yong Tang	5	5	28	1	0.30
978	Shelby Fletcher	2	1	82	18	0.30
979	Jun Fu	4	4	50	4	0.30
980	Ming Wan	5	5	24	2	0.30
981	Wei Huang	5	5	24	2	0.30
982	Juan Qiu	4	4	46	5	0.30
983	Lei Sun	5	5	20	3	0.30
984	Intermezzo Collective	3	3	53	12	0.30
985	Min Shen	5	4	22	7	0.30
986	Tao Chang	3	3	60	10	0.30
987	Fang Wei	5	5	23	2	0.30
988	Loretta Roberts	4	4	23	11	0.30
989	Ming Cao	4	4	45	5	0.30
990	Horst Barkholz	4	4	37	7	0.30
991	The Seekers	4	4	37	7	0.30
992	Emir Yohannan	2	2	37	25	0.30
993	Juan Xue	5	5	22	2	0.30
994	Joe Taylor	4	4	44	5	0.30
995	Juan Lei	5	5	18	3	0.30
996	Yang Long	4	4	40	6	0.30
997	Fang Pan	5	5	25	1	0.30
998	Li Hu	5	5	25	1	0.30
999	Xiuying Jin	4	4	36	7	0.30
1000	Qiang Peng	5	4	27	5	0.30
1001	Chao Dai	5	5	24	1	0.30
1002	Jie Yin	5	4	30	4	0.30
1003	Xiuying Gu	4	4	46	4	0.30
1004	Anne Miles	1	1	94	18	0.30
1005	Martial Harmonies	5	5	20	2	0.30
1006	Rebekka Lehmann	4	4	31	8	0.30
1007	Jun Cui	5	5	23	1	0.30
1008	Li Feng	4	4	41	5	0.29
1009	Samuel Gutierrez	4	4	41	5	0.29
1010	Guiying Mo	5	5	15	3	0.29
1011	Sean Rodgers	4	4	37	6	0.29
1012	Hazel Kara	5	5	11	4	0.29
1013	Jam & Honey	5	5	11	4	0.29
1014	Midnight Recursion	5	5	18	2	0.29
1015	Kenneth Fox	2	2	62	17	0.29
1016	Yang Xiang	3	3	47	12	0.29
1017	Artur Nurkiewicz	5	5	6	5	0.29
1018	Roberto Pausini	4	4	17	11	0.29
1019	Xiulan Yang	3	3	61	8	0.29
1020	Guiying Peng	4	4	35	6	0.29
1021	Jing Meng	4	4	35	6	0.29
1022	Xiuying Hou	4	4	35	6	0.29
1023	Xiuying Cui	3	3	46	12	0.29
1024	Fang Liu	4	4	42	4	0.29
1025	Marek Oleksa	5	5	5	5	0.29
1026	Cyprian Ziniewicz	5	4	11	8	0.29
1027	Konrad Goss	4	4	12	12	0.29
1028	Kornel Preś	4	4	12	12	0.29
1029	Wei Meng	5	5	23	0	0.29
1030	Ming Fan	4	4	34	6	0.29
1031	Ming Qiu	4	4	34	6	0.29
1032	Xiuying Du	4	3	51	6	0.29
1033	Leslie Barnes	3	3	56	9	0.29
1034	Teresa Williams	3	3	56	9	0.29
1035	Yong Gong	3	3	56	9	0.29
1036	Wei Ma	2	2	74	13	0.29
1037	Wei Hu	4	4	37	5	0.29
1038	Xiulan Fu	4	4	37	5	0.29
1039	Yan Gao	4	3	54	5	0.29
1040	Asuka Takahashi	5	5	11	3	0.29
1041	Qiang Luo	5	4	28	3	0.29
1042	Qiang Qiao	4	4	33	6	0.29
1043	Michelle Vallenduuk	5	5	7	4	0.29
1044	Fang Ren	5	5	18	1	0.29
1045	Yang Mo	5	5	18	1	0.29
1046	Guiying Chen	3	3	51	10	0.29
1047	Xiulan Jiang	5	5	14	2	0.29
1048	Erkan Barkholz	5	4	20	5	0.29
1049	Xia Lu	3	3	62	7	0.29
1050	The Brine Choir	7	4	6	0	0.29
1051	Chao Ding	4	4	36	5	0.29
1052	Insurgencia	3	3	32	15	0.29
1053	Jörg Niemeier	5	5	17	1	0.29
1054	Yan Xia	2	1	56	22	0.29
1055	Ping Ren	3	3	61	7	0.28
1056	Xiuying Zhong	4	4	24	8	0.28
1057	Ping Zhang	4	4	35	5	0.28
1058	Scott Johnson	4	4	35	5	0.28
1059	Laura Clark	5	5	9	3	0.28
1060	Li Mo	5	4	26	3	0.28
1061	Na Xu	3	3	53	9	0.28
1062	Ming Dai	5	5	16	1	0.28
1063	Min Ding	4	3	44	7	0.28
1064	Min Zhou	4	4	34	5	0.28
1065	Na Xie	4	4	34	5	0.28
1066	Narrow Passages	4	4	34	5	0.28
1067	Fang Gao	5	5	19	0	0.28
1068	Ping Tang	3	3	52	9	0.28
1069	Yan Wang	5	5	15	1	0.28
1070	Ante Wagenknecht	2	2	74	12	0.28
1071	Mariah Ramirez	4	4	37	4	0.28
1072	Yong Deng	5	5	11	2	0.28
1073	Ping Hou	3	3	59	7	0.28
1074	Silver Veil	8	3	4	0	0.28
1075	Tina Raymond	3	3	55	8	0.28
1076	Li Ren	4	4	40	3	0.28
1077	Jennifer Diaz	3	3	51	9	0.28
1078	Stefan Berus	5	5	10	2	0.28
1079	Cassette Future	7	4	2	0	0.28
1080	Brian Hughes	5	5	6	3	0.28
1081	Jun Zheng	5	5	17	0	0.28
1082	Yang Cheng	5	5	17	0	0.28
1083	Wei Ye	5	5	13	1	0.28
1084	Jeffrey Munoz PhD	2	2	61	15	0.28
1085	Thomas Salinas	2	2	61	15	0.28
1086	James Taylor	5	5	9	2	0.28
1087	Xia Guo	3	3	57	7	0.28
1088	Xia Hu	5	4	26	2	0.28
1089	Erin Fox	4	4	31	5	0.28
1090	Jun Tan	4	4	31	5	0.28
1091	Wei He	4	4	31	5	0.28
1092	Michaela Kelley	5	4	11	6	0.28
1093	Miguel Johnston	5	4	11	6	0.28
1094	Jie Kang	5	5	16	0	0.28
1095	Amy Lee	4	4	27	6	0.28
1096	Samuel Robertson	4	4	27	6	0.28
1097	Juan Xiang	4	3	44	6	0.28
1098	Yong Zhang	5	4	29	1	0.28
1099	Nela Melcer	4	4	8	11	0.28
1100	Yong Mao	4	4	30	5	0.28
1101	John Garcia	3	3	41	11	0.28
1102	Matthew Cowan	3	3	41	11	0.28
1103	Rusty Anvil	3	3	41	11	0.28
1104	Jie Tan	3	2	58	11	0.28
1105	Sophie Ramirez	5	5	15	0	0.28
1106	Xiulan Tang	5	5	15	0	0.28
1107	Jun Gong	5	4	28	1	0.28
1108	Wei Zhang	4	4	33	4	0.28
1109	Xiulan Duan	4	4	33	4	0.28
1110	Aleksander Przybyś	5	5	7	2	0.28
1111	Fang Tan	5	5	7	2	0.28
1112	Abdullah Bauer	1	1	99	13	0.27
1113	Barbara Haas	1	1	99	13	0.27
1114	Birgid Junck-Sauer	1	1	99	13	0.27
1115	Brandi Tran	1	1	99	13	0.27
1116	Brenda Hammond	1	1	99	13	0.27
1117	David Clements	1	1	99	13	0.27
1118	David Patel	1	1	99	13	0.27
1119	Gabriel Ruppert	1	1	99	13	0.27
1120	Hans-Christian Drub	1	1	99	13	0.27
1121	Keith Navarro	1	1	99	13	0.27
1122	Susan Haney	1	1	99	13	0.27
1123	Carolyn Hall	4	4	21	7	0.27
1124	Rachel Stein	2	2	69	12	0.27
1125	Fang Feng	4	4	32	4	0.27
1126	Li Zhou	4	4	32	4	0.27
1127	Matthew Yoder	4	3	23	11	0.27
1128	Na Ye	4	4	28	5	0.27
1129	Yan Qian	2	2	39	20	0.27
1130	Dariusz Olszowa	5	5	2	3	0.27
1131	Verdant Pulse	4	3	30	9	0.27
1132	Maciej Siejak	5	4	4	7	0.27
1133	Fang Hou	4	4	31	4	0.27
1134	Yang Xiao	4	4	31	4	0.27
1135	Maya Jensen	5	5	12	0	0.27
1136	Fang Gu	4	4	34	3	0.27
1137	Na Song	4	4	34	3	0.27
1138	Christine Ward	3	3	45	9	0.27
1139	Lisa Hofmann	3	3	45	9	0.27
1140	Sonora Flux	3	3	45	9	0.27
1141	Lei Cheng	2	2	67	12	0.27
1142	Guiying Tang	4	4	30	4	0.27
1143	Yong Yu	4	4	30	4	0.27
1144	Jie Jia	5	4	21	2	0.27
1145	Ping Gong	4	4	37	2	0.27
1146	Gregory Williams	3	3	48	8	0.27
1147	Fang Jia	4	4	33	3	0.27
1148	Tao Jiang	4	4	33	3	0.27
1149	Scott Sanders	3	3	44	9	0.27
1150	Jun Qiu	2	2	66	12	0.27
1151	Malwina Penar	4	4	29	4	0.27
1152	Michael Harris	4	4	29	4	0.27
1153	Xia Yu	3	3	51	7	0.27
1154	Xia Jin	4	3	42	5	0.27
1155	Wei Tian	4	4	32	3	0.27
1156	Heino Ladeck	4	4	17	7	0.27
1157	Xia Qiu	4	4	28	4	0.27
1158	Qiang Meng	4	3	34	7	0.27
1159	Anthony Gutierrez	2	2	61	13	0.27
1160	Alfred Thibault	3	3	35	11	0.27
1161	Synaptic Stream	3	3	35	11	0.27
1162	Zoé-Agnès Delaunay	3	3	35	11	0.27
1163	Finn McGraw	5	5	9	0	0.27
1164	William Tidewell	5	5	9	0	0.27
1165	Chao Yuan	4	4	31	3	0.27
1166	Jun Su	4	4	31	3	0.27
1167	Ping Du	4	4	31	3	0.27
1168	Juan Zhu	4	4	27	4	0.27
1169	Xia Song	4	4	27	4	0.27
1170	Elizabeth Saunders	4	4	23	5	0.26
1171	Jing Liao	4	4	30	3	0.26
1172	Juan Tan	4	4	30	3	0.26
1173	Na Zhu	4	4	30	3	0.26
1174	Tao Mo	4	4	30	3	0.26
1175	Xia Yao	4	4	30	3	0.26
1176	Yan Li	4	4	30	3	0.26
1177	Marcel Derek	3	3	41	9	0.26
1178	Wei Yu	3	3	41	9	0.26
1179	Ming Xiao	5	4	21	1	0.26
1180	Xiuying Guo	4	4	26	4	0.26
1181	Final Order	4	4	22	5	0.26
1182	Tao Huang	4	4	33	2	0.26
1183	Wei Tan	4	4	33	2	0.26
1184	Wei Dai	4	3	39	5	0.26
1185	Juan Lai	5	4	24	0	0.26
1186	Lei Jia	4	4	29	3	0.26
1187	Min Yao	4	4	29	3	0.26
1188	Na Liao	4	4	29	3	0.26
1189	Walter White	4	4	29	3	0.26
1190	Xiulan Deng	5	4	20	1	0.26
1191	Xiuying Lin	5	4	20	1	0.26
1192	Jennifer Gibson	2	2	62	12	0.26
1193	Laura Brown	2	2	62	12	0.26
1194	Gordon Cox	4	4	25	4	0.26
1195	Fang Qian	4	4	32	2	0.26
1196	Coralia Bellweather	5	5	6	0	0.26
1197	Lei Pan	4	4	28	3	0.26
1198	Craig Cameron	4	4	24	4	0.26
1199	Jun Wen	4	4	35	1	0.26
1200	Eugene Henderson	3	3	46	7	0.26
1201	Psycho Twang	3	3	46	7	0.26
1202	David Hayes	4	4	20	5	0.26
1203	Luciana Iacovelli	1	1	68	19	0.26
1204	Fang Zheng	4	4	31	2	0.26
1205	Wei Peng	4	3	37	5	0.26
1206	Kendra Rivera	3	3	42	8	0.26
1207	Joker's Smile	4	4	16	6	0.26
1208	Renee Williams	4	4	16	6	0.26
1209	Echoes of Arcadia	4	4	27	3	0.26
1210	Ming Li	4	4	27	3	0.26
1211	Yan Chen	3	3	38	9	0.26
1212	Jie Wan	4	4	23	4	0.26
1213	Neon Horizon	4	4	23	4	0.26
1214	Xiulan Cai	4	4	23	4	0.26
1215	Ming Xu	3	3	45	7	0.26
1216	Na Hu	3	3	45	7	0.26
1217	Ping Cui	3	3	45	7	0.26
1218	Lisa Lopez	2	2	67	10	0.26
1219	Xia Fang	4	4	30	2	0.26
1220	Yang Yang	4	4	30	2	0.26
1221	Chao Jiang	4	3	36	5	0.26
1222	Ronald Kennedy	3	3	41	8	0.26
1223	Ewan MacCrae	5	5	4	0	0.26
1224	Freya Lindholm	5	5	4	0	0.26
1225	Jun Du	4	4	26	3	0.26
1226	Ming Hu	4	3	43	3	0.26
1227	Cordula Ehlert	2	2	59	12	0.26
1228	Xia Tan	4	4	33	1	0.26
1229	Michael Holloway	3	3	44	7	0.26
1230	Fiona Mercer	5	5	3	0	0.26
1231	Liam O'Sullivan	5	5	3	0	0.26
1232	Ping Deng	4	4	25	3	0.26
1233	Xiulan Kang	4	4	25	3	0.26
1234	Anouk Schatteleijn	3	3	36	9	0.26
1235	Li Wu	4	4	17	5	0.25
1236	Dawn Gross	2	2	65	10	0.25
1237	John Gray	2	2	65	10	0.25
1238	Gang Hao	4	4	28	2	0.25
1239	Guiying Jiang	4	4	28	2	0.25
1240	Jing Song	4	3	34	5	0.25
1241	Guiying Hao	3	3	50	5	0.25
1242	Alexis Ruiz	1	1	98	10	0.25
1243	Brian Ballard	1	1	98	10	0.25
1244	Candice Santiago	1	1	98	10	0.25
1245	David Bates Jr.	1	1	98	10	0.25
1246	Eric Shelton	1	1	98	10	0.25
1247	George Williams	1	1	98	10	0.25
1248	Jared Newman	1	1	98	10	0.25
1249	Jodi Miller	1	1	98	10	0.25
1250	Jonathan Butler	1	1	98	10	0.25
1251	Joris Heerschop-Weylant	1	1	98	10	0.25
1252	Laura Christian	1	1	98	10	0.25
1253	Lauren Mccoy	1	1	98	10	0.25
1254	Paige Case	1	1	98	10	0.25
1255	Richard Mendoza	1	1	98	10	0.25
1256	Robert Lee	1	1	98	10	0.25
1257	Robert Washington	1	1	98	10	0.25
1258	Roger Porter	1	1	98	10	0.25
1259	Shelby Dixon	1	1	98	10	0.25
1260	Tanya Pittman	1	1	98	10	0.25
1261	Thomas Miller	1	1	98	10	0.25
1262	Tony Olson	1	1	98	10	0.25
1263	Virginia Warner	1	1	98	10	0.25
1264	Chao Xie	2	2	57	12	0.25
1265	Ping Tao	4	4	20	4	0.25
1266	Karin Dippel	4	4	16	5	0.25
1267	Guiying Jin	4	4	27	2	0.25
1268	Michael Moore	4	4	27	2	0.25
1269	Guiying Lin	3	3	38	8	0.25
1270	Jun Yan	4	4	23	3	0.25
1271	Lei Lai	4	4	23	3	0.25
1272	Wei Lu	4	4	23	3	0.25
1273	Dierk Bohlander	3	3	23	12	0.25
1274	Li Sun	3	2	51	9	0.25
1275	Na Yan	3	3	41	7	0.25
1276	Lei Zeng	3	2	47	10	0.25
1277	Christine Lam	4	4	15	5	0.25
1278	Jing Duan	4	4	26	2	0.25
1279	Jing Lei	4	4	26	2	0.25
1280	Christian Mcpherson	4	4	11	6	0.25
1281	Lei Yao	4	4	22	3	0.25
1282	Ping Shao	4	4	22	3	0.25
1283	Jie Yao	4	3	39	3	0.25
1284	Ryan Ford	4	4	7	7	0.25
1285	Hilmar Hänel	4	3	24	7	0.25
1286	Qiang Liu	3	3	40	7	0.25
1287	Gang Xie	4	4	25	2	0.25
1288	Lei Cai	4	4	25	2	0.25
1289	Wei Pan	4	4	25	2	0.25
1290	Na Chang	4	4	21	3	0.25
1291	Yang Han	4	4	21	3	0.25
1292	Jing Shi	4	4	28	1	0.25
1293	Jun Huang	4	4	28	1	0.25
1294	Yong Liao	3	3	39	7	0.25
1295	Jie Feng	4	4	24	2	0.25
1296	Jing Zhu	4	4	24	2	0.25
1297	Jun Xiang	4	4	24	2	0.25
1298	Shawn Fox	4	4	24	2	0.25
1299	Tao Yuan	4	4	24	2	0.25
1300	Wei Liang	4	4	24	2	0.25
1301	Xia Bai	4	4	24	2	0.25
1302	Guiying Zhang	3	3	35	8	0.25
1303	Holly Gonzalez	4	4	9	6	0.25
1304	Sovereign Elect	4	4	9	6	0.25
1305	Fang Yi	4	4	20	3	0.25
1306	Juan Cai	4	4	20	3	0.25
1307	Jun Cai	4	4	20	3	0.25
1308	Xia Wan	3	3	42	6	0.25
1309	Collettivo Moderno	2	2	53	12	0.25
1310	Lori Lee	4	4	16	4	0.25
1311	Li Kong	4	4	27	1	0.25
1312	Na Wu	4	3	33	4	0.25
1313	Jie Ma	3	3	49	4	0.25
1314	Wei Lin	4	4	23	2	0.25
1315	Guiying Fu	4	3	29	5	0.25
1316	Lisa Smith	3	3	23	11	0.25
1317	Matthew Moss	3	3	34	8	0.25
1318	Dhanuk Hari	4	4	8	6	0.25
1319	Hansh Sidhu	4	4	8	6	0.25
1320	Jie Tian	4	4	19	3	0.25
1321	Li Zhong	4	4	19	3	0.25
1322	Xiulan Tan	3	3	41	6	0.25
1323	Xia Wang	4	3	32	4	0.24
1324	Fang Qiao	3	3	48	4	0.24
1325	Ming Shen	4	4	22	2	0.24
1326	Li Tian	3	3	33	8	0.24
1327	Fang Dong	4	4	18	3	0.24
1328	Yumiko Ito	4	4	18	3	0.24
1329	Giuliana Costanzi	4	4	14	4	0.24
1330	Maura Chiappetta	4	4	14	4	0.24
1331	Chao Qian	4	4	25	1	0.24
1332	Silvia Sölzer-van der Dussen	3	3	25	10	0.24
1333	Chao Jin	4	4	21	2	0.24
1334	Dominic Phillips	4	4	21	2	0.24
1335	Jie Wang	4	4	21	2	0.24
1336	Li Tan	4	4	21	2	0.24
1337	Li Yi	4	4	21	2	0.24
1338	Min Wu	4	4	21	2	0.24
1339	Yan Xiao	4	4	21	2	0.24
1340	Karen Anderson	4	3	38	2	0.24
1341	Jie Lei	3	3	39	6	0.24
1342	Fang Xiao	4	4	24	1	0.24
1343	Tao Yu	4	4	24	1	0.24
1344	Greta Löchel-Neureuther	3	3	35	7	0.24
1345	Silvana Radisch	3	3	35	7	0.24
1346	Xiuying Cao	3	3	35	7	0.24
1347	Jing Zhou	4	4	20	2	0.24
1348	Ming Luo	4	4	20	2	0.24
1349	Xiulan Ma	4	4	20	2	0.24
1350	Jillian Salazar	2	2	49	12	0.24
1351	Jie Dong	4	4	23	1	0.24
1352	Yan Yang	4	4	23	1	0.24
1353	John Rogers	4	3	29	4	0.24
1354	Xiulan Zhou	4	3	29	4	0.24
1355	Midnight Harvest	3	3	34	7	0.24
1356	Denise Schroeder	4	4	8	5	0.24
1357	Sydney Adams	4	4	8	5	0.24
1358	Antonietta Ceravolo-Condoleo	4	4	19	2	0.24
1359	Fang Shen	4	4	19	2	0.24
1360	Jing Feng	4	4	19	2	0.24
1361	Razor's Lament	4	4	19	2	0.24
1362	Lei Fang	3	2	47	8	0.24
1363	Yang Luo	4	4	15	3	0.24
1364	Astrid Lecoq	4	4	11	4	0.24
1365	Kläre Schacht-Mielcarek	4	4	11	4	0.24
1366	Élodie Albert	4	4	11	4	0.24
1367	Li Wen	4	4	22	1	0.24
1368	Tao He	4	4	22	1	0.24
1369	James Reyes	3	3	44	4	0.24
1370	Brandon Vasquez	4	4	7	5	0.24
1371	Hex Collective	4	4	18	2	0.24
1372	Jing Ding	4	4	18	2	0.24
1373	Juan Yan	4	4	18	2	0.24
1374	Jun Wang	4	4	18	2	0.24
1375	David Miles	3	3	29	8	0.24
1376	Domenico Gasperi	3	3	40	5	0.24
1377	Jie Du	3	3	40	5	0.24
1378	Jie Wu	3	3	40	5	0.24
1379	Rozalia Ciepluch	1	1	40	23	0.24
1380	Jeffrey Phillips	4	4	14	3	0.24
1381	Lei Gao	4	4	14	3	0.24
1382	Marcus Douglas	1	1	62	17	0.24
1383	Thomas Massey	1	1	62	17	0.24
1384	Min Zou	4	3	31	3	0.24
1385	Wei Zeng	4	2	37	6	0.24
1386	Snout Noise	3	3	36	6	0.24
1387	Mathew Chapman III	3	2	42	9	0.24
1388	Guiying Su	4	4	21	1	0.24
1389	Jie Gu	4	4	21	1	0.24
1390	Li Fan	4	4	21	1	0.24
1391	Wei Dong	4	4	21	1	0.24
1392	Wei Wu	4	4	21	1	0.24
1393	Allison Sanford	1	1	54	19	0.24
1394	Cindy Smith	1	1	54	19	0.24
1395	Szymon Piguła	1	1	54	19	0.24
1396	Yan Guo	4	4	17	2	0.24
1397	Yong Qian	4	4	17	2	0.24
1398	Encrucijada	4	4	13	3	0.24
1399	Michelangelo Berlusconi	4	4	13	3	0.24
1400	Bogdan Gehringer	3	3	35	6	0.24
1401	Juan Tao	3	3	35	6	0.24
1402	Li Shen	3	3	35	6	0.24
1403	Ming Gu	3	3	35	6	0.24
1404	Ming Zhu	3	3	35	6	0.24
1405	Wendy Brown	3	3	35	6	0.24
1406	Xiuying Tian	4	4	20	1	0.23
1407	Tao Tang	4	3	22	5	0.23
1408	Chloë de Gruil	4	4	12	3	0.23
1409	Frau Adelgunde Hermighausen	4	4	12	3	0.23
1410	Ottmar Grein Groth-Butte	4	4	12	3	0.23
1411	Qiang Mo	4	3	29	3	0.23
1412	Ascendant	4	4	8	4	0.23
1413	Maria Freeman	4	4	8	4	0.23
1414	Philip Conley	2	2	56	9	0.23
1415	Fang Ma	4	4	19	1	0.23
1416	Juan Meng	4	4	19	1	0.23
1417	Lei Jiang	4	4	19	1	0.23
1418	Na Kang	4	4	19	1	0.23
1419	Yang Ma	4	4	19	1	0.23
1420	Yong Hu	4	4	19	1	0.23
1421	Kathleen Davis	3	3	19	10	0.23
1422	Jing Yin	4	4	15	2	0.23
1423	Li Shao	4	4	15	2	0.23
1424	Daniel Roberts	2	2	48	11	0.23
1425	Jessica Stafford	2	2	48	11	0.23
1426	Margaret Sanchez	2	2	48	11	0.23
1427	Andrej Ruppersberger	4	4	11	3	0.23
1428	Chao Qin	4	4	11	3	0.23
1429	Honoré Foucher	4	4	11	3	0.23
1430	Kevin Baker	4	4	11	3	0.23
1431	Maura Jovinelli	4	4	11	3	0.23
1432	Zachary Francis	4	4	11	3	0.23
1433	Lunar Drift	3	2	39	9	0.23
1434	Synaptic Collapse	4	4	7	4	0.23
1435	Shared Secrets	3	3	29	7	0.23
1436	Yong Guo	2	2	51	10	0.23
1437	Janina Kałek	4	4	14	2	0.23
1438	Lei Zhu	4	4	14	2	0.23
1439	Coriolano Luria-Scialpi	3	3	25	8	0.23
1440	Wulf Lübs	3	3	36	5	0.23
1441	Yan Chang	3	3	36	5	0.23
1442	Alexa Albers-Säuberlich	3	3	32	6	0.23
1443	Nordisk Resonans	3	3	32	6	0.23
1444	Peter Jacobi Jäckel	3	3	32	6	0.23
1445	Prisma Flux	3	3	32	6	0.23
1446	Samantha Herring	3	3	32	6	0.23
1447	Scott Mckinney	3	3	32	6	0.23
1448	Terry Gonzalez	3	3	32	6	0.23
1449	Jamie Ramos	4	4	6	4	0.23
1450	Ricky Silva	4	4	6	4	0.23
1451	Sonic Pulse	4	4	6	4	0.23
1452	Min Yin	4	4	17	1	0.23
1453	Yang Yi	4	4	17	1	0.23
1454	Gang Zeng	4	3	23	4	0.23
1455	Amanda Blake	3	3	39	4	0.23
1456	Tao Song	3	3	39	4	0.23
1457	Qiang Ye	3	3	35	5	0.23
1458	Xia Fan	3	3	35	5	0.23
1459	Yang Gao	3	3	35	5	0.23
1460	Yang Kong	4	4	20	0	0.23
1461	Qiang Xiao	4	3	26	3	0.23
1462	Li Tang	3	3	31	6	0.23
1463	Ping Cao	3	3	31	6	0.23
1464	Thomas Harrison	3	3	31	6	0.23
1465	Beth Carpenter	3	2	37	9	0.23
1466	Seth Smith	3	2	37	9	0.23
1467	Jie Dai	2	2	53	9	0.23
1468	Gang Ye	4	4	16	1	0.23
1469	Li Ye	4	4	16	1	0.23
1470	Léon Pasquier	4	4	16	1	0.23
1471	Yoko Fujita	4	4	16	1	0.23
1472	Denise Johnson	1	1	71	13	0.23
1473	Donna Malone	1	1	71	13	0.23
1474	Gang Tao	1	1	71	13	0.23
1475	Tao Zhu	4	4	19	0	0.23
1476	Justin King	3	3	30	6	0.23
1477	Ming Su	2	2	52	9	0.23
1478	Xiulan Shao	4	4	15	1	0.23
1479	Xiuying Yan	4	4	15	1	0.23
1480	Crimson Carriage	7	2	4	0	0.23
1481	Rainfall Fiction	6	3	3	0	0.23
1482	Gang Hu	3	3	33	5	0.23
1483	Guiying Yi	3	3	33	5	0.23
1484	Janine Berger	4	4	7	3	0.23
1485	Ming Cai	4	4	7	3	0.23
1486	Jun Tian	4	4	18	0	0.23
1487	Gang Mao	4	4	14	1	0.22
1488	Li Cui	4	4	14	1	0.22
1489	Tao Zou	4	4	14	1	0.22
1490	Xiulan Zou	4	4	14	1	0.22
1491	Mutual Orbit	2	2	47	10	0.22
1492	Qiang Kang	4	4	10	2	0.22
1493	Rhea Wali	4	4	10	2	0.22
1494	Adrian Carroll	2	2	21	17	0.22
1495	Christopher Rice	3	3	32	5	0.22
1496	Fallen Cherubs	3	3	32	5	0.22
1497	Yang Guo	3	3	32	5	0.22
1498	Nichole Martinez	2	2	32	14	0.22
1499	William Mcclain	4	4	6	3	0.22
1500	Ellen Ford	4	3	12	6	0.22
1501	Chao Bai	4	4	17	0	0.22
1502	Chao Ye	4	3	23	3	0.22
1503	Ping Cheng	3	3	28	6	0.22
1504	Thomas Walsh	2	2	50	9	0.22
1505	Coastal Horizon	4	4	13	1	0.22
1506	Crimson Theorem	4	4	13	1	0.22
1507	Lei Lu	4	4	13	1	0.22
1508	Na Zhang	3	3	31	5	0.22
1509	Ursus	3	3	31	5	0.22
1510	Sylas Dune	5	4	0	0	0.22
1511	Min Chang	4	4	16	0	0.22
1512	Pamela Chan	3	3	27	6	0.22
1513	Guiying Yin	4	3	18	4	0.22
1514	Nicole Sudak	4	3	18	4	0.22
1515	Henriette Gonzalez-Grégoire	1	1	60	15	0.22
1516	Laurent-Théodore Humbert	1	1	60	15	0.22
1517	Philip Schneider	1	1	60	15	0.22
1518	Édith Deschamps Le Clément	1	1	60	15	0.22
1519	Chao Jia	3	3	34	4	0.22
1520	Gang Kang	3	3	34	4	0.22
1521	Steven Smith	3	3	34	4	0.22
1522	Wei Chang	3	3	34	4	0.22
1523	Samar Agarwal	1	1	34	22	0.22
1524	Stephanie Robinson	4	4	8	2	0.22
1525	Stephen Meyer	4	4	8	2	0.22
1526	Juan Shao	4	3	25	2	0.22
1527	Surveillance	3	3	30	5	0.22
1528	Jing Shen	3	3	37	3	0.22
1529	Min Dai	3	3	37	3	0.22
1530	Shirley Riley	3	3	22	7	0.22
1531	Tao Cai	3	3	33	4	0.22
1532	Buzz Collective	4	4	7	2	0.22
1533	Diana Taylor	4	4	7	2	0.22
1534	Franck Prévost de la Bertrand	4	4	7	2	0.22
1535	Kayla Gomez	4	4	7	2	0.22
1536	Pénélope du Cohen	4	4	7	2	0.22
1537	Ming Chang	3	3	29	5	0.22
1538	Anita Hamerla	4	4	3	3	0.22
1539	Xia Tang	4	4	14	0	0.22
1540	Maria Black	3	3	25	6	0.22
1541	Yong Hao	2	2	36	12	0.22
1542	Xia Feng	3	3	32	4	0.22
1543	Jun Chen	3	3	28	5	0.22
1544	Danielle Walsh	2	2	28	14	0.22
1545	Li Zhang	2	2	28	14	0.22
1546	Kumiko Miura	4	4	2	3	0.22
1547	Ashley Yang	2	2	50	8	0.22
1548	Christopher Andrews	2	2	50	8	0.22
1549	Jeffrey Herring	2	2	50	8	0.22
1550	Li Li	4	4	13	0	0.22
1551	Wei Mao	4	4	13	0	0.22
1552	Yan Gong	3	3	24	6	0.22
1553	Yong Xue	3	3	35	3	0.22
1554	Juan Lu	2	2	46	9	0.22
1555	Xiuying Duan	4	3	22	2	0.22
1556	Arco Luminoso	3	3	27	5	0.22
1557	Ming Du	3	3	27	5	0.22
1558	Philippine Laurent	3	3	27	5	0.22
1559	Xia Qiao	3	3	27	5	0.22
1560	Ming Wen	4	4	12	0	0.22
1561	Lei Tao	4	2	35	3	0.22
1562	Aneta Pruschke-Sölzer	3	3	23	6	0.22
1563	Jessica Peck	3	3	23	6	0.22
1564	Maria Carlson	3	3	23	6	0.22
1565	Na Qin	3	3	23	6	0.22
1566	Katelyn Johnson	3	3	34	3	0.22
1567	Sarah Wheeler	3	3	34	3	0.22
1568	Donald Medina	2	2	45	9	0.21
1569	Erik Mathews	2	2	45	9	0.21
1570	Joshua Orozco	2	2	45	9	0.21
1571	Sander Veenstra	2	2	45	9	0.21
1572	Estación Sonora	3	3	30	4	0.21
1573	Guiying Gu	3	3	30	4	0.21
1574	Yang Fan	3	3	30	4	0.21
1575	Yang Liu	2	2	30	13	0.21
1576	Christina Johnson	4	4	4	2	0.21
1577	Cynthia Murphy	4	4	4	2	0.21
1578	Derek Smith	4	4	4	2	0.21
1579	Julie Davis	4	4	4	2	0.21
1580	Raissa Anders	4	4	4	2	0.21
1581	Kimberly Stanton	3	3	26	5	0.21
1582	Yan Jia	3	3	26	5	0.21
1583	Xia Pan	4	3	17	3	0.21
1584	Luigina Camuccini	3	3	22	6	0.21
1585	Yan Song	3	3	22	6	0.21
1586	Scott Snyder	2	2	33	12	0.21
1587	Jie Li	3	3	29	4	0.21
1588	Lei Huang	3	3	29	4	0.21
1589	Marina Acedo Montaña	3	3	29	4	0.21
1590	Selma Flantz	3	3	29	4	0.21
1591	Velvet Haze	3	3	29	4	0.21
1592	Yang Zeng	3	3	25	5	0.21
1593	Samantha Mills	2	2	47	8	0.21
1594	Yan Tao	2	2	47	8	0.21
1595	Maria Tyler	3	3	21	6	0.21
1596	Gang Gao	2	2	54	6	0.21
1597	Leandro Artigas Benítez	3	3	28	4	0.21
1598	Xia Tian	3	3	28	4	0.21
1599	Chao Kong	3	2	34	7	0.21
1600	Steven Buchanan	4	4	2	2	0.21
1601	Yong Fan	4	3	19	2	0.21
1602	David Moore	3	3	24	5	0.21
1603	Xiulan Zhong	3	3	24	5	0.21
1604	Yan Fu	3	3	35	2	0.21
1605	Yong Cui	2	2	46	8	0.21
1606	Xia Zhang	4	4	9	0	0.21
1607	Rolando Gagliardi	4	2	21	6	0.21
1608	Amber Bates	1	1	57	14	0.21
1609	April Schmidt	1	1	57	14	0.21
1610	Cody Patterson	1	1	57	14	0.21
1611	Colleen Velazquez	1	1	57	14	0.21
1612	Jeremy Park	1	1	57	14	0.21
1613	William Keller	1	1	57	14	0.21
1614	Alfredo Ajello	3	2	37	6	0.21
1615	Jun Liu	3	2	37	6	0.21
1616	Ethereal Chorus	2	2	49	7	0.21
1617	Makayla Stone	4	3	14	3	0.21
1618	Chao Fang	3	3	30	3	0.21
1619	Fang Wan	3	3	30	3	0.21
1620	Jun Deng	3	3	30	3	0.21
1621	Xiulan Xiang	2	2	41	9	0.21
1622	Chao Song	4	3	17	2	0.21
1623	Dennis Bush	3	3	22	5	0.21
1624	Rebecca Chapman	2	2	22	14	0.21
1625	Gang Lai	2	2	44	8	0.21
1626	Levi Holloway	4	4	7	0	0.21
1627	Jie Shen	3	3	29	3	0.21
1628	Juan Zhang	3	3	29	3	0.21
1629	Ming Liang	3	3	29	3	0.21
1630	Yan Kong	3	3	29	3	0.21
1631	Ming Shi	2	2	40	9	0.21
1632	Kelly Johnson	2	2	51	6	0.21
1633	Fang Hao	3	3	25	4	0.21
1634	Michael Pratt	3	3	25	4	0.21
1635	Xiulan Feng	3	3	25	4	0.21
1636	Ming Jia	5	2	17	2	0.21
1637	Charles Wright	2	2	32	11	0.21
1638	Jonah Calloway	4	4	6	0	0.21
1639	Marin Thorne	4	4	6	0	0.21
1640	Na Feng	4	3	23	0	0.21
1641	Tao Meng	3	3	28	3	0.21
1642	Wei Guo	3	3	28	3	0.21
1643	Michael Sexton	2	2	39	9	0.20
1644	Robert Jenkins	2	2	39	9	0.20
1645	Jie Xie	4	3	19	1	0.20
1646	Min Peng	3	3	24	4	0.20
1647	Puccio Bixio-Gatto	3	3	24	4	0.20
1648	Taichi Watanabe	3	3	24	4	0.20
1649	Kamila Ornat	2	2	20	14	0.20
1650	Marie James	2	2	20	14	0.20
1651	Mary Rodriguez	2	2	20	14	0.20
1652	Eckehard Wagner	3	3	16	6	0.20
1653	Juan Hu	3	3	27	3	0.20
1654	Ming Song	3	2	44	3	0.20
1655	Hansjürgen Börner	2	2	38	9	0.20
1656	Michael Gomez	2	2	49	6	0.20
1657	Jie He	3	3	23	4	0.20
1658	Whispered Embers	3	3	23	4	0.20
1659	Kristen Parks	3	3	19	5	0.20
1660	Whisper the Thistles	3	3	19	5	0.20
1661	Jing Li	3	3	30	2	0.20
1662	Megan Sandoval	2	2	30	11	0.20
1663	The Phantom Operators	5	3	5	0	0.20
1664	Astrid Nørgaard	4	4	4	0	0.20
1665	Barnaby Quill	4	4	4	0	0.20
1666	Jonas Fenwick	4	4	4	0	0.20
1667	Marina Grey	4	4	4	0	0.20
1668	Orin Caldwell	4	4	4	0	0.20
1669	Robin Jones	4	4	4	0	0.20
1670	Selena Marrow	4	4	4	0	0.20
1671	Silas Finch	4	4	4	0	0.20
1672	Thorne Rivenhall	4	4	4	0	0.20
1673	Lei Dai	3	3	26	3	0.20
1674	Fang Lai	2	2	48	6	0.20
1675	Christopher Murray	1	1	59	12	0.20
1676	James Rubio	1	1	59	12	0.20
1677	Meredith Walker	1	1	59	12	0.20
1678	Rebecca Gonzales	1	1	59	12	0.20
1679	Laura Braun	2	2	33	10	0.20
1680	Lisa Miller	2	2	44	7	0.20
1681	Stacey Taylor	3	3	14	6	0.20
1682	Diana Mitchell	1	1	62	11	0.20
1683	James Hall	1	1	62	11	0.20
1684	Kevin Lopez	1	1	62	11	0.20
1685	Jing Tang	3	3	25	3	0.20
1686	Juan Zheng	3	3	25	3	0.20
1687	Li Xu	3	3	25	3	0.20
1688	Min Tan	3	3	25	3	0.20
1689	Xiulan Huang	3	3	25	3	0.20
1690	Chao Chang	4	3	16	1	0.20
1691	Jie Yi	3	3	21	4	0.20
1692	Lei Lin	3	3	21	4	0.20
1693	Emmy Bonbach	4	3	12	2	0.20
1694	Herr Heinz-Walter Scheuermann	4	3	12	2	0.20
1695	Jie Cheng	4	3	12	2	0.20
1696	Paige Adams	3	3	13	6	0.20
1697	Ping Tan	3	3	24	3	0.20
1698	Qiang Zeng	3	3	24	3	0.20
1699	Stephanie Huhn	3	3	24	3	0.20
1700	Xiuying Ma	3	3	24	3	0.20
1701	Li Jiang	3	3	27	2	0.20
1702	Tao Liu	3	3	27	2	0.20
1703	Xiuying Ren	3	3	27	2	0.20
1704	Bertram Hübel	3	3	23	3	0.20
1705	Gang Tang	3	3	23	3	0.20
1706	Guiying Xiao	3	3	23	3	0.20
1707	Lei Shao	3	3	23	3	0.20
1708	Lei Shi	3	3	23	3	0.20
1709	Li Zhao	3	3	23	3	0.20
1710	Ming Wei	3	3	23	3	0.20
1711	Jie Mao	3	3	19	4	0.20
1712	Tracy Lawson	2	2	41	7	0.20
1713	Andrew Miller	1	1	52	13	0.20
1714	Robert Martinez	1	1	52	13	0.20
1715	Samuel Morrison	1	1	52	13	0.20
1716	Iron Cathedral	7	1	3	0	0.20
1717	Chao Wang	3	3	26	2	0.20
1718	Fang Jiang	3	3	26	2	0.20
1719	Oscar Marshall	3	3	26	2	0.20
1720	Ping Xie	3	3	26	2	0.20
1721	Xia Lei	3	3	26	2	0.20
1722	Xiuying Qin	3	3	26	2	0.20
1723	Kristýna Horváthová	4	4	0	0	0.20
1724	Fiesta Nocturna	3	3	22	3	0.20
1725	Harmonic Collective	3	3	22	3	0.20
1726	Robert Pham	3	3	22	3	0.20
1727	Rómulo Huertas	3	3	22	3	0.20
1728	Tao Du	3	3	22	3	0.20
1729	Tao Kong	3	3	22	3	0.20
1730	Xiulan Yin	3	3	22	3	0.20
1731	Yan Hou	3	3	22	3	0.20
1732	Yang Dai	3	3	22	3	0.20
1733	Qiang Cheng	4	3	13	1	0.19
1734	Olga Kosałka	3	3	7	7	0.19
1735	Preset Protocol	3	3	7	7	0.19
1736	Flora Karz	3	3	18	4	0.19
1737	Glide	3	3	18	4	0.19
1738	Ryan Cohen	3	3	18	4	0.19
1739	Yong Ding	3	3	18	4	0.19
1740	Guiying Dong	3	3	29	1	0.19
1741	Yui Fukuda	3	3	14	5	0.19
1742	Jing Deng	3	3	25	2	0.19
1743	Mira Everly	5	3	0	0	0.19
1744	Juan Johnson	3	3	21	3	0.19
1745	Lei Du	3	3	21	3	0.19
1746	Na Mo	3	3	21	3	0.19
1747	Carl Jones	2	2	32	9	0.19
1748	Christina Bowers	2	2	32	9	0.19
1749	Beverly Olsen	1	1	54	12	0.19
1750	Lisa Woods	1	1	54	12	0.19
1751	Rei Aoki	1	1	54	12	0.19
1752	Vasyl Jaroš	3	2	23	7	0.19
1753	Sem de Swart-van Voorst	2	2	39	7	0.19
1754	Antonietta Zaccagnini	3	3	13	5	0.19
1755	Gang Cai	3	3	24	2	0.19
1756	Gang Tian	3	3	24	2	0.19
1757	Jing Du	3	3	24	2	0.19
1758	Ming Hou	3	3	24	2	0.19
1759	Qiang Feng	3	3	24	2	0.19
1760	Xiulan Meng	3	3	24	2	0.19
1761	Ashley Andrade	1	1	57	11	0.19
1762	Darrell Wilson	1	1	57	11	0.19
1763	Laura Moore	1	1	57	11	0.19
1764	Timothy Lin	1	1	57	11	0.19
1765	Xia Long	3	3	20	3	0.19
1766	Ingbert Hertrampf	2	2	31	9	0.19
1767	Patricia-Julie Lopes	3	3	16	4	0.19
1768	Guiying Ma	3	3	23	2	0.19
1769	Jordan Baxter	3	3	23	2	0.19
1770	Natasza Dydo	3	3	23	2	0.19
1771	Yang Liang	3	3	23	2	0.19
1772	Katrina Hendricks	3	3	8	6	0.19
1773	Edward Siering	3	3	19	3	0.19
1774	Jun Yuan	3	3	19	3	0.19
1775	Lunar Pulse	3	3	19	3	0.19
1776	Vicki Morgan	3	3	19	3	0.19
1777	Gerlind Sager-Ortmann	3	2	36	3	0.19
1778	Wei Tao	2	2	41	6	0.19
1779	Yang Cao	2	2	41	6	0.19
1780	Jonathan Diaz	4	3	10	1	0.19
1781	Timothy Rios	3	3	15	4	0.19
1782	Brandi Daugherty	1	1	63	9	0.19
1783	David Mueller	1	1	63	9	0.19
1784	Donna Davidson	1	1	63	9	0.19
1785	Jennifer Thomas	1	1	63	9	0.19
1786	Joseph Choi	1	1	63	9	0.19
1787	Joseph Huang	1	1	63	9	0.19
1788	Linda Williams	1	1	63	9	0.19
1789	Peter Johnson	1	1	63	9	0.19
1790	Raymond Marshall	1	1	63	9	0.19
1791	Shannon Hughes	1	1	63	9	0.19
1792	Stacy Saunders	1	1	63	9	0.19
1793	Tiffany Acevedo	1	1	63	9	0.19
1794	Adalbert Adolph	3	3	11	5	0.19
1795	Chao Cui	3	3	22	2	0.19
1796	Na Bai	3	3	22	2	0.19
1797	Wei Yin	3	3	22	2	0.19
1798	Xiulan Gu	3	3	22	2	0.19
1799	Xiulan Jia	3	3	22	2	0.19
1800	Xiuying Wang	3	3	22	2	0.19
1801	Yan Cui	3	3	22	2	0.19
1802	Civic Ensemble	2	2	44	5	0.19
1803	Elizabeth Wagner	2	2	44	5	0.19
1804	Bianca Sagnelli-Onio	3	3	18	3	0.19
1805	Christine Wall	3	3	18	3	0.19
1806	Fernanda Guarneri	3	3	18	3	0.19
1807	Ping Lei	3	3	18	3	0.19
1808	Rhythmic Echo	3	3	18	3	0.19
1809	Leslie Moore	2	2	29	9	0.19
1810	Archipelago	3	3	14	4	0.19
1811	Yang Zou	3	3	25	1	0.19
1812	Chao Shi	3	3	21	2	0.19
1813	Guiying Zheng	3	3	21	2	0.19
1814	Jie Zhong	3	3	21	2	0.19
1815	Jun Gu	3	3	21	2	0.19
1816	Jun Shen	3	3	21	2	0.19
1817	Tao Xiong	3	3	21	2	0.19
1818	Tao Zhao	3	2	38	2	0.19
1819	Christopher Davis	3	3	17	3	0.19
1820	Jing Xiang	3	3	17	3	0.19
1821	Rhythm Brothers	3	3	17	3	0.19
1822	Yong Zhong	4	2	25	1	0.19
1823	Akemi Nakajima	3	3	13	4	0.19
1824	Annetta Passalacqua	4	3	4	2	0.19
1825	Olaf Szumny	3	3	9	5	0.19
1826	Chao Cheng	3	3	20	2	0.19
1827	Chao Shen	3	3	20	2	0.19
1828	Min Yi	3	3	20	2	0.19
1829	Yang Qiao	3	3	20	2	0.19
1830	Yong Dai	3	3	20	2	0.19
1831	Konrad Meissner	3	3	16	3	0.19
1832	Na Luo	3	3	16	3	0.19
1833	Sovereign Riders	3	3	16	3	0.19
1834	Fang Xie	2	2	38	6	0.18
1835	Min Xu	2	2	38	6	0.18
1836	Clinton Fischer	1	1	49	12	0.18
1837	David Allison	1	1	49	12	0.18
1838	Erica Lopez	1	1	49	12	0.18
1839	Franck Joly	1	1	49	12	0.18
1840	Melissa Rivas	1	1	49	12	0.18
1841	Benoît-Zacharie Gilbert	3	3	12	4	0.18
1842	Bianca Weber	3	3	12	4	0.18
1843	Margaud Chevallier	3	3	12	4	0.18
1844	Nathalie Jacquot	3	3	12	4	0.18
1845	Sly Notion	3	3	12	4	0.18
1846	Jing Hu	2	2	34	7	0.18
1847	Luminescence	3	3	8	5	0.18
1848	Marc-Raymond Durand	3	3	8	5	0.18
1849	Sync Shift	3	3	8	5	0.18
1850	Bureaucracy	3	3	19	2	0.18
1851	Karl-Otto Käster	3	3	19	2	0.18
1852	Robert Stephens	3	3	19	2	0.18
1853	Xiulan Xie	3	3	19	2	0.18
1854	Yang Xie	3	3	19	2	0.18
1855	Phantom Roots	2	2	30	8	0.18
1856	Lei Wei	3	3	15	3	0.18
1857	Yan Wu	3	3	15	3	0.18
1858	Stacie Bennett	2	2	37	6	0.18
1859	Pier Pavone-Cignaroli	1	1	48	12	0.18
1860	Charles Chase	3	3	11	4	0.18
1861	Clara Seco-Pelayo	3	3	11	4	0.18
1862	Lina Mendizábal Robledo	3	2	17	7	0.18
1863	Qiang Sun	3	3	22	1	0.18
1864	Xiulan Hu	3	3	22	1	0.18
1865	Reginald Wallace	2	2	33	7	0.18
1866	Audrey-Suzanne Ribeiro	3	3	18	2	0.18
1867	Guiying Qiu	3	3	18	2	0.18
1868	Jason Roberts	3	3	18	2	0.18
1869	Juan Yuan	3	3	18	2	0.18
1870	Lei Deng	2	2	40	5	0.18
1871	Jing Fang	2	2	36	6	0.18
1872	Xia Hao	2	2	36	6	0.18
1873	Maggie Faivre	3	3	10	4	0.18
1874	Cardboard Twenty-Five	3	3	21	1	0.18
1875	Chao Zheng	3	3	21	1	0.18
1876	Guiying Xia	3	3	21	1	0.18
1877	Yang Lai	3	3	21	1	0.18
1878	Paul Lewis	2	2	32	7	0.18
1879	Brandon Ramirez	3	3	17	2	0.18
1880	Ming Lai	3	3	17	2	0.18
1881	Xiulan Zhang	3	3	17	2	0.18
1882	James Jackson	3	2	34	2	0.18
1883	Gang Liu	3	3	13	3	0.18
1884	Guiying Chang	3	3	20	1	0.18
1885	Lei Qiao	3	3	20	1	0.18
1886	Min Wang	3	3	20	1	0.18
1887	Tao Gu	3	3	20	1	0.18
1888	Tao Long	3	3	20	1	0.18
1889	Wei Li	3	3	20	1	0.18
1890	Anthony King	3	3	16	2	0.18
1891	Echo Syntax	3	3	16	2	0.18
1892	Fang Zhu	3	3	16	2	0.18
1893	Ippazio Barcella	3	3	16	2	0.18
1894	Lei Zhong	3	3	16	2	0.18
1895	Nicole Scott	3	3	16	2	0.18
1896	Ping Dong	3	3	16	2	0.18
1897	Wei Qian	3	3	16	2	0.18
1898	Forjadura	3	2	33	2	0.18
1899	Tammy Chen	3	2	33	2	0.18
1900	Michelle Ellis	2	2	27	8	0.18
1901	Jolanda Salz	3	3	12	3	0.18
1902	Luna Spark	3	3	12	3	0.18
1903	Sayuri Sato	3	3	12	3	0.18
1904	Yang Deng	3	3	12	3	0.18
1905	David Schultz	2	2	34	6	0.18
1906	Jacqueline Dickson	2	2	34	6	0.18
1907	Jonathan Young	2	2	34	6	0.18
1908	Marianna Stefanik	3	3	8	4	0.18
1909	Fang Song	3	3	19	1	0.18
1910	Gang Qian	3	3	19	1	0.18
1911	Lei Xiao	3	3	19	1	0.18
1912	Na Cao	3	3	19	1	0.18
1913	Na Meng	3	3	19	1	0.18
1914	Tao Ding	3	3	19	1	0.18
1915	Xiulan Chang	3	3	19	1	0.18
1916	Jessica Lopez	2	2	30	7	0.18
1917	Jie Zhou	2	2	30	7	0.18
1918	Celestial Horizon	3	3	15	2	0.18
1919	Gang Zou	3	3	15	2	0.18
1920	Jing Zou	3	3	15	2	0.18
1921	Ping Qiao	3	3	15	2	0.18
1922	Ping Yu	3	3	15	2	0.18
1923	Ping Yuan	3	3	15	2	0.18
1924	Yan Lei	3	3	15	2	0.18
1925	Tao Qin	2	2	37	5	0.18
1926	Mark Martin	1	1	48	11	0.18
1927	Shawn Koch	1	1	48	11	0.18
1928	William Hansen	1	1	48	11	0.18
1929	Augustin Besnard-Imbert	3	3	11	3	0.18
1930	Diaspora Rising	3	3	11	3	0.18
1931	Dominique Perret	3	3	11	3	0.18
1932	Erdmute Hermann-Karge	3	3	11	3	0.18
1933	Gundi Barkholz	3	3	11	3	0.18
1934	Kevin Carney	3	3	11	3	0.18
1935	Lidia Szpyt	3	3	11	3	0.18
1936	Yan Zhao	3	3	22	0	0.18
1937	Kaylee Freeman	2	2	22	9	0.18
1938	Gang Huang	2	2	33	6	0.18
1939	David Stewart	1	1	44	12	0.18
1940	Juan Watkins	1	1	44	12	0.18
1941	Sheri Brooks	1	1	44	12	0.18
1942	Frau Marianna Wernecke	3	3	18	1	0.18
1943	Juan Chen	3	3	18	1	0.18
1944	Jun Qin	3	3	18	1	0.18
1945	Na Han	3	3	18	1	0.18
1946	Qiang Yu	3	3	18	1	0.18
1947	Guiying Fan	3	3	14	2	0.18
1948	Karl-Ludwig Kensy	2	2	25	8	0.18
1949	Tidal Reverie	5	2	6	0	0.18
1950	Jorge Hood	2	2	36	5	0.18
1951	Keith Hernandez	2	2	36	5	0.18
1952	Rossana Colletti	2	2	36	5	0.18
1953	Stephanie Silva	2	2	36	5	0.18
1954	Lisa Castillo	3	3	10	3	0.18
1955	Joseph Trujillo	2	2	32	6	0.17
1956	Juan Su	3	3	17	1	0.17
1957	Juan Zhou	3	3	17	1	0.17
1958	Xiuying Yu	3	3	17	1	0.17
1959	Lorenzo Morabito	3	2	23	4	0.17
1960	Cosmic Drift	3	3	13	2	0.17
1961	Julie Reynolds	3	3	13	2	0.17
1962	Jun Li	3	3	13	2	0.17
1963	Kathryn Ward	3	3	13	2	0.17
1964	Lei Peng	3	3	13	2	0.17
1965	Li Wei	3	3	13	2	0.17
1966	Tao Qiao	3	3	13	2	0.17
1967	Amber Allen	2	2	24	8	0.17
1968	Vperyod Siyanie!	2	2	24	8	0.17
1969	Yan Dai	2	2	35	5	0.17
1970	Jennifer Jackson	1	1	46	11	0.17
1971	Nancy Pierce	1	1	46	11	0.17
1972	Alex Halama	3	3	9	3	0.17
1973	Anatol Dietz	3	3	9	3	0.17
1974	Evelyn Hernandez	3	3	9	3	0.17
1975	Karina Bąbel	3	3	9	3	0.17
1976	Kathryn Sherman	3	3	9	3	0.17
1977	Whispered Strings	3	3	9	3	0.17
1978	Donna Ryan	3	3	20	0	0.17
1979	Charles Delahaye	2	2	31	6	0.17
1980	Juan Cheng	3	3	16	1	0.17
1981	Jun Long	3	3	16	1	0.17
1982	Na Jin	3	3	16	1	0.17
1983	Ping Jin	3	3	16	1	0.17
1984	Ryan Adams	3	3	16	1	0.17
1985	Wei Kang	3	3	16	1	0.17
1986	Xiuying Kang	3	3	16	1	0.17
1987	Xiuying Qian	3	3	16	1	0.17
1988	Adriana Figueroa	1	1	49	10	0.17
1989	Charles Hall	1	1	49	10	0.17
1990	James Mosley	1	1	49	10	0.17
1991	Thomas Dudley	1	1	49	10	0.17
1992	Justin Williams	3	3	12	2	0.17
1993	Ping Peng	3	3	12	2	0.17
1994	Rust & Reverie	5	2	4	0	0.17
1995	Selkie's Hollow	5	2	4	0	0.17
1996	Xiuying Sun	2	2	34	5	0.17
1997	Christopher Reed	3	3	8	3	0.17
1998	Jennifer Savage	3	3	8	3	0.17
1999	Jovens Sujos	3	3	8	3	0.17
2000	Rudolf Malý	3	3	8	3	0.17
2001	Stephen Bell	3	3	8	3	0.17
2002	Yong Bai	3	3	8	3	0.17
2003	Xia Chang	3	3	19	0	0.17
2004	Yang Duan	3	2	25	3	0.17
2005	Juan Gu	2	2	30	6	0.17
2006	Fang Chang	3	3	15	1	0.17
2007	Jing Wei	3	3	15	1	0.17
2008	Jun Feng	3	3	15	1	0.17
2009	Jun Hu	3	3	15	1	0.17
2010	Jun Peng	3	3	15	1	0.17
2011	Lei Xiong	3	3	15	1	0.17
2012	Min Wan	3	3	15	1	0.17
2013	Ming Zhong	3	3	15	1	0.17
2014	Nanami Yoshida	3	3	15	1	0.17
2015	Qiang Deng	3	3	15	1	0.17
2016	Tao Jia	3	3	15	1	0.17
2017	Xia Yang	3	3	15	1	0.17
2018	Xiuying Xia	3	3	15	1	0.17
2019	Yong Xia	3	3	11	2	0.17
2020	Violet Engines	5	2	3	0	0.17
2021	Emily Taylor	2	2	33	5	0.17
2022	Juan Zeng	2	2	33	5	0.17
2023	Na Yuan	2	2	33	5	0.17
2024	Tao Mao	2	2	33	5	0.17
2025	Jasmine Atkins	3	3	7	3	0.17
2026	Kazuya Aoki	3	3	7	3	0.17
2027	Antonietta Kreusel-Seip	3	3	18	0	0.17
2028	Xiuying Xu	3	3	18	0	0.17
2029	Kelli Williamson	2	2	29	6	0.17
2030	William Coleman	2	2	29	6	0.17
2031	Gabriel Malon	3	3	3	4	0.17
2032	Debra Martin	3	2	9	7	0.17
2033	Chao Lai	3	3	14	1	0.17
2034	Chao Liao	3	3	14	1	0.17
2035	Christine Jones	3	3	14	1	0.17
2036	Jing Kong	3	3	14	1	0.17
2037	Juan Fang	3	3	14	1	0.17
2038	Ping Guo	3	3	14	1	0.17
2039	Tao Li	3	3	14	1	0.17
2040	Tao Zheng	3	3	14	1	0.17
2041	David Rodriguez	1	1	47	10	0.17
2042	Rita Morrison	1	1	47	10	0.17
2043	Bhamini Saini	3	3	10	2	0.17
2044	Fabian Dłubak	3	3	10	2	0.17
2045	Lei Gu	3	3	10	2	0.17
2046	Leslie Mathis	3	3	10	2	0.17
2047	Patricia Hardy	3	3	10	2	0.17
2048	Phantom Syntax	3	3	10	2	0.17
2049	Turbo Kill	5	2	2	0	0.17
2050	Ming Hao	2	2	32	5	0.17
2051	Fang Xue	3	3	6	3	0.17
2052	Logan Rogers	3	3	6	3	0.17
2053	Megan Gilmore	3	3	6	3	0.17
2054	Lucas Potter	3	2	12	6	0.17
2055	Stormens Sång	3	2	12	6	0.17
2056	Jing Han	3	3	17	0	0.17
2057	Jun Yao	3	3	17	0	0.17
2058	Qiang Mao	3	3	17	0	0.17
2059	Yang Lei	3	3	17	0	0.17
2060	Peggy Krebs	2	2	28	6	0.17
2061	Na Zeng	3	3	13	1	0.17
2062	Paul Castro	3	3	13	1	0.17
2063	Tao Yan	3	3	13	1	0.17
2064	Wei Zhu	3	3	13	1	0.17
2065	Xiuying Zhou	3	3	13	1	0.17
2066	Loro	3	2	19	4	0.17
2067	Elizabeth Chase	2	2	35	4	0.17
2068	Heather Lutz	3	3	9	2	0.17
2069	Nirvaan Barman	3	3	9	2	0.17
2070	Anna Shaw	2	2	31	5	0.17
2071	Deborah Rogers	2	2	31	5	0.17
2072	Kristin Miranda	2	2	31	5	0.17
2073	Obsidian Harmony	2	2	31	5	0.17
2074	Ping Chen	2	2	31	5	0.17
2075	Yan Deng	2	2	31	5	0.17
2076	Jacqueline Fields	3	3	5	3	0.17
2077	Inès Collet	3	3	16	0	0.17
2078	Juan Xia	3	3	16	0	0.17
2079	Wei Long	3	3	16	0	0.17
2080	Joshua Oneill	2	2	27	6	0.17
2081	Ming Qian	2	2	27	6	0.17
2082	The Cedar Lane Collective	2	2	27	6	0.17
2083	Chao Guo	3	3	12	1	0.17
2084	Judith Hunter	3	3	12	1	0.17
2085	Kenneth Crosby	3	3	12	1	0.17
2086	Paula Wilson	3	3	12	1	0.17
2087	Phantom Resonance	3	3	12	1	0.17
2088	Rachel Wilson	3	3	12	1	0.17
2089	Steven Branch	3	3	12	1	0.17
2090	Matthäus Ladeck	3	2	18	4	0.17
2091	Ida Kenig	2	2	12	10	0.17
2092	Darlene Galloway	2	2	34	4	0.17
2093	Melanie Fisher	2	2	34	4	0.17
2094	Bianca Peano	2	1	40	7	0.17
2095	Yang Kang	1	1	45	10	0.17
2096	Antoine Barthelemy	3	3	8	2	0.17
2097	Fixx'd	3	3	8	2	0.17
2098	Melina Tebaldi	3	3	8	2	0.17
2099	Richard Gutierrez	3	3	8	2	0.17
2100	Zara Trivedi	3	3	8	2	0.17
2101	Andrea Miller	2	2	19	8	0.17
2102	Holly Valenzuela	2	2	30	5	0.17
2103	Edit Hethur	3	3	4	3	0.16
2104	Janna Jäkel	3	3	4	3	0.16
2105	Kelly Jones	3	3	4	3	0.16
2106	Krista Wallace	3	3	4	3	0.16
2107	Margit Sölzer	3	3	4	3	0.16
2108	Parallel Static	3	3	4	3	0.16
2109	Paula Mercado	3	3	4	3	0.16
2110	Restless Verse	3	3	4	3	0.16
2111	Scott Richmond	3	3	4	3	0.16
2112	Herr Arno Ebert	1	1	52	8	0.16
2113	Jeffrey Roman	1	1	52	8	0.16
2114	Marlene Scholtz	1	1	52	8	0.16
2115	Patrick Grein Groth	1	1	52	8	0.16
2116	Veronika Haering	1	1	52	8	0.16
2117	Willibert Ebert	1	1	52	8	0.16
2118	Chao Xiong	3	3	15	0	0.16
2119	Ming Zou	3	3	15	0	0.16
2120	Nicholas Davis	2	2	26	6	0.16
2121	Anneliese Bolnbach	3	3	11	1	0.16
2122	Gang Duan	3	3	11	1	0.16
2123	Kevin Wong	3	3	11	1	0.16
2124	Michelle Byrd	3	3	11	1	0.16
2125	Valhalla's Fury	3	3	11	1	0.16
2126	Frederic Margraf	3	2	17	4	0.16
2127	Ming Tan	2	2	22	7	0.16
2128	Wei Wen	2	2	33	4	0.16
2129	Yong Han	2	2	33	4	0.16
2130	Amber Gibson	3	3	7	2	0.16
2131	David Miller	3	3	7	2	0.16
2132	Francisco Krein-Möchlichen	3	3	7	2	0.16
2133	Lawrence Cooper	3	3	7	2	0.16
2134	Mai Sato	3	3	7	2	0.16
2135	Margaret Martin	3	3	7	2	0.16
2136	Ming Bai	3	3	7	2	0.16
2137	Ping Mao	3	3	7	2	0.16
2138	Urban Decay Theorem	3	3	7	2	0.16
2139	Brittany Kelly	2	2	29	5	0.16
2140	Christopher Roberts	2	2	29	5	0.16
2141	Fang Han	3	3	14	0	0.16
2142	Friedo Dobes	3	3	14	0	0.16
2143	Ming Shao	3	3	14	0	0.16
2144	Ming Yuan	3	3	14	0	0.16
2145	Tao Pan	3	3	14	0	0.16
2146	Lei Kang	3	2	20	3	0.16
2147	Nicole Robinson	2	2	14	9	0.16
2148	Brandon Mays	2	2	25	6	0.16
2149	Jessica Macdonald	2	2	25	6	0.16
2150	Crimson Harvest	3	3	10	1	0.16
2151	Fang Dai	3	3	10	1	0.16
2152	Jun He	3	3	10	1	0.16
2153	Li Shi	3	3	10	1	0.16
2154	Min Fan	3	3	10	1	0.16
2155	Richard Johnson	3	3	10	1	0.16
2156	Min Zhang	2	2	32	4	0.16
2157	Antonius Faust	3	3	6	2	0.16
2158	Carlo Mazzacurati	3	3	6	2	0.16
2159	Jeremy Li	3	3	6	2	0.16
2160	Joseph Burgess	3	3	6	2	0.16
2161	Misfit Bistro	3	3	6	2	0.16
2162	Rosaria Pedersoli	3	3	6	2	0.16
2163	Stephanie Garcia	3	3	6	2	0.16
2164	Jacob Harris	3	2	12	5	0.16
2165	Brandi Bates	2	2	28	5	0.16
2166	Fang Tian	3	3	13	0	0.16
2167	Juan Mao	3	3	13	0	0.16
2168	Lei Qian	3	3	13	0	0.16
2169	Ping Gu	3	3	13	0	0.16
2170	Barbara Mckee	3	3	9	1	0.16
2171	Elizabeth Johnson	3	3	9	1	0.16
2172	Erin Welch	3	3	9	1	0.16
2173	Immortal Spark	3	3	9	1	0.16
2174	Jie Liang	3	3	9	1	0.16
2175	Jing Luo	3	3	9	1	0.16
2176	Regina Bruce	3	3	9	1	0.16
2177	Min Yan	2	2	31	4	0.16
2178	Yang Wen	2	2	31	4	0.16
2179	Artisans of Rhythm	3	3	5	2	0.16
2180	Christopher Thomas	3	3	5	2	0.16
2181	Jessica Harvey	3	3	5	2	0.16
2182	Joann Johnson	3	3	5	2	0.16
2183	Laure du Hebert	3	3	5	2	0.16
2184	Michael Maddox	3	3	5	2	0.16
2185	Nathalie Legrand	3	3	5	2	0.16
2186	Pavlína Macháčková	3	3	5	2	0.16
2187	Pompeo Turchi	3	3	5	2	0.16
2188	Radiant Siblings	3	3	5	2	0.16
2189	Rati Deo	3	3	5	2	0.16
2190	Rebel Scream	3	3	5	2	0.16
2191	Sarah Gardner	3	3	5	2	0.16
2192	Timothy Bray	3	3	5	2	0.16
2193	David Johnson	3	2	22	2	0.16
2194	Angelina Ortmann	2	2	27	5	0.16
2195	Janet Margraf	2	2	27	5	0.16
2196	Juan Kong	2	2	27	5	0.16
2197	Danielle Gray	3	3	12	0	0.16
2198	Jun Gao	3	3	12	0	0.16
2199	Laurent Breton	3	3	12	0	0.16
2200	Lisa Hart	3	3	12	0	0.16
2201	Qiang Huang	3	3	12	0	0.16
2202	Astrid Gomes	3	2	18	3	0.16
2203	Li Chang	3	2	18	3	0.16
2204	Cesare Cicilia	2	2	23	6	0.16
2205	Yong Ren	2	2	23	6	0.16
2206	Diane Dunn	1	1	45	9	0.16
2207	Na Liang	1	1	45	9	0.16
2208	Gabor Salz	3	3	8	1	0.16
2209	Francesca Krebs	2	2	19	7	0.16
2210	Ronnie Cook	2	2	19	7	0.16
2211	Min Liu	2	2	30	4	0.16
2212	Amy Tate	3	3	4	2	0.16
2213	Antoine Perrot-Léger	3	3	4	2	0.16
2214	Charlotte Lefèvre	3	3	4	2	0.16
2215	Christine Bonnin-Morel	3	3	4	2	0.16
2216	Hridaan Kaur	3	3	4	2	0.16
2217	Matthieu Le Roux	3	3	4	2	0.16
2218	Operatic Insurgence	3	3	4	2	0.16
2219	Phantom Sync	3	3	4	2	0.16
2220	Rohan Shanker	3	3	4	2	0.16
2221	Rémy Denis de Duhamel	3	3	4	2	0.16
2222	Spectral Junk	3	3	4	2	0.16
2223	Talvikierros	3	3	4	2	0.16
2224	The Three Drafts	3	3	4	2	0.16
2225	Berend Suijker-Tirie	2	2	26	5	0.16
2226	Yang Yuan	2	2	26	5	0.16
2227	Heinz-Joachim Renner	1	1	37	11	0.16
2228	Chao Gao	3	3	11	0	0.16
2229	Fang Yuan	3	3	11	0	0.16
2230	Guiying He	3	3	11	0	0.16
2231	Jing Gong	3	3	11	0	0.16
2232	Ming Tian	3	3	11	0	0.16
2233	Ping Su	3	3	11	0	0.16
2234	Wei Qiu	3	3	11	0	0.16
2235	Xia Han	3	3	11	0	0.16
2236	Guiying Lai	3	3	7	1	0.16
2237	Juan Ma	3	3	7	1	0.16
2238	Juan Ye	3	3	7	1	0.16
2239	Michel Meyer	3	3	7	1	0.16
2240	Undetected	3	3	7	1	0.16
2241	Xiuying Liang	3	3	7	1	0.16
2242	Li Du	2	2	29	4	0.16
2243	Subtle Noise	2	2	29	4	0.16
2244	Inaaya Bhatia	3	3	3	2	0.16
2245	Kendra Green	3	3	3	2	0.16
2246	Nehmat Kanda	3	3	3	2	0.16
2247	Paula Bonbach	3	3	3	2	0.16
2248	Quantum Sonic Architects	3	3	3	2	0.16
2249	Robert Woods	3	3	3	2	0.16
2250	Split Persona	3	3	3	2	0.16
2251	Wieslaw Wulf	3	3	3	2	0.16
2252	Qiang Ding	3	2	20	2	0.16
2253	Ping Wen	2	2	25	5	0.16
2254	Angela Gonzalez	1	1	36	11	0.16
2255	Nicole Gonzalez	1	1	36	11	0.16
2256	Gang Xiang	3	3	10	0	0.16
2257	Guiying Cui	3	3	10	0	0.16
2258	Wei Jia	3	3	10	0	0.16
2259	Thibault Hardy	2	2	21	6	0.16
2260	Brandon Santos	2	1	27	9	0.16
2261	Brian Palmer	2	2	32	3	0.16
2262	Brittany Bush	2	2	32	3	0.16
2263	William Fuentes	2	2	32	3	0.16
2264	Bryan Garcia	3	3	6	1	0.16
2265	Colette-Jeannine Lemaître	3	3	6	1	0.16
2266	Crystal Cruz	3	3	6	1	0.16
2267	Electric Novella	3	3	6	1	0.16
2268	Emmanuel de Blot	3	3	6	1	0.16
2269	Kimberly Roberts	3	3	6	1	0.16
2270	Meik Plath	3	3	6	1	0.16
2271	Raymond Chartier	3	3	6	1	0.16
2272	Simone Parent	3	3	6	1	0.16
2273	Triassic Collective	3	3	6	1	0.16
2274	Valerie Brown	3	3	6	1	0.16
2275	Gang Dai	3	2	23	1	0.16
2276	Fang Qiu	2	2	28	4	0.16
2277	Jun Zhu	2	2	28	4	0.16
2278	Min Xue	2	2	28	4	0.16
2279	Tony Rhodes	2	2	28	4	0.16
2280	Michael Ross	3	3	2	2	0.16
2281	Wayne Malone	3	2	19	2	0.16
2282	Guiying Tan	2	2	24	5	0.16
2283	Gang Lin	3	3	9	0	0.15
2284	Juan Peng	3	3	9	0	0.15
2285	Vincent Morrison	2	2	20	6	0.15
2286	Capucine Pasquier	3	3	5	1	0.15
2287	Li Fu	3	3	5	1	0.15
2288	Ludovico Mondadori	3	3	5	1	0.15
2289	Martino Toninelli	3	3	5	1	0.15
2290	Yong Zou	3	2	22	1	0.15
2291	Kathleen Cunningham	2	2	16	7	0.15
2292	April Johnson	2	2	27	4	0.15
2293	Lara Wohlgemut	2	2	27	4	0.15
2294	Li Zou	2	2	27	4	0.15
2295	Operatic Pulse	2	2	27	4	0.15
2296	Kenichi Kobayashi	3	3	1	2	0.15
2297	Kari Sutton	1	1	45	8	0.15
2298	Jade Thompson	3	3	8	0	0.15
2299	Lila "Lilly" Hartman	3	3	8	0	0.15
2300	Adèle Bonneau	2	2	19	6	0.15
2301	Leah Anderson	2	2	19	6	0.15
2302	Wei Qiao	2	2	30	3	0.15
2303	Corinne du Bazin	3	3	4	1	0.15
2304	Dawn Ramsey	3	3	4	1	0.15
2305	Double Chance	3	3	4	1	0.15
2306	Jacob Boyd	3	3	4	1	0.15
2307	Marianne Sanchez-Hebert	3	3	4	1	0.15
2308	Sarah James	3	3	4	1	0.15
2309	Susan Mcclain	3	3	4	1	0.15
2310	Tracey Jordan	3	3	4	1	0.15
2311	Adrien Gérard	3	2	10	4	0.15
2312	Michel Fournier-Robert	3	2	10	4	0.15
2313	Guiying Gong	3	2	21	1	0.15
2314	Guiying Xie	3	2	17	2	0.15
2315	Ming Wang	3	2	17	2	0.15
2316	Mikołaj Rosłoń	3	3	7	0	0.15
2317	Na Hou	3	3	7	0	0.15
2318	Chao Zhang	2	2	29	3	0.15
2319	Amanda Hill	3	3	3	1	0.15
2320	Joseph Vincent	3	3	3	1	0.15
2321	Kaja Juroszek	3	3	3	1	0.15
2322	Karol Jereczek	3	3	3	1	0.15
2323	Lueur	3	3	3	1	0.15
2324	Timothy Burke	3	3	3	1	0.15
2325	Jonathan Wade	3	2	16	2	0.15
2326	Christopher Rosales	2	2	21	5	0.15
2327	Kuno Grein Groth	2	2	21	5	0.15
2328	Tara Jennings	2	2	21	5	0.15
2329	Wei Xia	2	2	21	5	0.15
2330	Dawn Nelson	2	2	32	2	0.15
2331	Arlo Sterling	3	3	6	0	0.15
2332	Cassian Rae	3	3	6	0	0.15
2333	Elara May	3	3	6	0	0.15
2334	Eva-Marie Seidel-Otto	3	3	6	0	0.15
2335	Lyra Blaze	3	3	6	0	0.15
2336	Orion Cruz	3	3	6	0	0.15
2337	William Roux	3	1	29	3	0.15
2338	Jing Hou	2	2	28	3	0.15
2339	Michael Richard	2	2	28	3	0.15
2340	Ryan Palmer	2	2	28	3	0.15
2341	Gioacchino Gargallo	2	1	34	6	0.15
2342	Randolf Fiebig	2	1	34	6	0.15
2343	Dorothée Rousset	1	1	39	9	0.15
2344	Amy White	3	3	2	1	0.15
2345	Eric Lewis	3	3	2	1	0.15
2346	William Jones	3	3	2	1	0.15
2347	Marija Lehmann	2	2	13	7	0.15
2348	John Hernandez	2	2	24	4	0.15
2349	Juan Shen	2	2	24	4	0.15
2350	Kimberly Ross	2	2	24	4	0.15
2351	Stacy Henderson	2	2	24	4	0.15
2352	Matthew Miller	1	1	24	13	0.15
2353	Gang Xia	3	2	15	2	0.15
2354	Daniel Rogers	2	2	31	2	0.15
2355	Caleb Foster	3	3	5	0	0.15
2356	Eleanor Wren	3	3	5	0	0.15
2357	Kai Reynolds	3	3	5	0	0.15
2358	Michael Smith	3	3	5	0	0.15
2359	Moral Friction	3	3	5	0	0.15
2360	Gunther Hendriks	2	2	16	6	0.15
2361	Gang Yao	2	2	27	3	0.15
2362	Guiying Duan	2	2	27	3	0.15
2363	Lotte Arens	2	2	27	3	0.15
2364	Ming Kong	3	2	18	1	0.15
2365	Xia Ma	3	2	18	1	0.15
2366	Erin Shah	2	2	23	4	0.15
2367	James Moore	2	2	23	4	0.15
2368	Li Yao	2	2	23	4	0.15
2369	Karol Klęczar	2	2	8	8	0.15
2370	Robin Russell	2	2	19	5	0.15
2371	Theresa Pena	1	1	19	14	0.15
2372	William Mcfarland	1	1	19	14	0.15
2373	The Wave Riders	4	2	5	0	0.15
2374	Audrey Johnson	1	1	41	8	0.15
2375	Dhruv Mall	1	1	41	8	0.15
2376	Áurea Escobar-Jara	1	1	41	8	0.15
2377	Ivy Steele	3	3	4	0	0.15
2378	Mikayla Cook	3	3	4	0	0.15
2379	Nate Wild	3	3	4	0	0.15
2380	Scarlett Moon	3	3	4	0	0.15
2381	Kristina Gonzales	2	2	15	6	0.15
2382	Danielle Luna	2	1	21	9	0.15
2383	John Pitts	2	2	26	3	0.15
2384	Xia Ye	3	2	17	1	0.15
2385	Juan Bai	2	2	22	4	0.15
2386	Nocturnal Drifters	2	2	22	4	0.15
2387	Xiulan Fang	2	2	22	4	0.15
2388	Amber Murray	1	1	44	7	0.15
2389	Autumn Moore	1	1	44	7	0.15
2390	Gabrielle Wright	1	1	44	7	0.15
2391	Kimberly Zimmerman	1	1	44	7	0.15
2392	Melissa Brown	1	1	44	7	0.15
2393	Stacy Kennedy	1	1	44	7	0.15
2394	Tina Jackson	1	1	44	7	0.15
2395	Liquid Prism	3	2	13	2	0.15
2396	Echoes of Eternity	3	3	3	0	0.14
2397	Sandra Green	3	3	3	0	0.14
2398	Tamara Solis	3	3	3	0	0.14
2399	Gregg Fuentes	3	2	9	3	0.14
2400	Mineral Breath Asphalt	3	2	9	3	0.14
2401	Jordan Collins	2	2	14	6	0.14
2402	Chao Gong	2	2	25	3	0.14
2403	Fang Qin	2	2	25	3	0.14
2404	Xiuying Luo	2	2	25	3	0.14
2405	Yang Xue	2	2	25	3	0.14
2406	Ellen Riley	1	1	25	12	0.14
2407	Silja Jacob	1	1	25	12	0.14
2408	Yang Yan	3	2	16	1	0.14
2409	Dean Stevens	2	2	21	4	0.14
2410	Ming Ye	2	2	21	4	0.14
2411	Stormheart	2	2	21	4	0.14
2412	Xiulan Zhu	2	2	21	4	0.14
2413	Xia Zheng	2	2	17	5	0.14
2414	Siren's Call	4	2	3	0	0.14
2415	Michael Bell	1	1	39	8	0.14
2416	Richard Hill	1	1	39	8	0.14
2417	Li Huang	3	2	19	0	0.14
2418	Ashley Cabrera	2	2	13	6	0.14
2419	Jazzy Wanderers	2	2	13	6	0.14
2420	Terrance Parker PhD	2	2	24	3	0.14
2421	Victoria Pierce	2	2	24	3	0.14
2422	Wei Sun	2	2	24	3	0.14
2423	Xiuying Chang	2	2	24	3	0.14
2424	Alexandra Levy	2	2	20	4	0.14
2425	Jing Xie	2	2	20	4	0.14
2426	Lauretta Tresoldi	2	2	20	4	0.14
2427	Michael Dean	2	2	20	4	0.14
2428	Adam Wallace	1	1	31	10	0.14
2429	Jennifer Stephens	1	1	31	10	0.14
2430	Yong Qiu	3	1	28	2	0.14
2431	Amanda Brock	2	2	16	5	0.14
2432	David Hernandez	2	2	16	5	0.14
2433	Karl-Heinz Bruder	2	2	16	5	0.14
2434	William Cruz	2	2	16	5	0.14
2435	Gundel Klapp	2	2	27	2	0.14
2436	Na Cai	2	2	27	2	0.14
2437	Carolyn Donovan	3	3	1	0	0.14
2438	Herr Serge Tröst	2	2	12	6	0.14
2439	Jeremi Kazberuk	2	2	12	6	0.14
2440	Chao Tang	2	2	23	3	0.14
2441	Jie Duan	2	2	23	3	0.14
2442	Min Bai	2	2	23	3	0.14
2443	Samuel Olsen	2	2	23	3	0.14
2444	The Wayfarers	2	2	23	3	0.14
2445	Xiuying Shen	2	2	23	3	0.14
2446	LyriCrest	1	1	45	6	0.14
2447	Melissa Miller	2	2	19	4	0.14
2448	Tao Duan	2	2	19	4	0.14
2449	Cosmic Reverberation	5	1	2	0	0.14
2450	Guiying Meng	2	2	26	2	0.14
2451	Timothy Schmidt	2	2	26	2	0.14
2452	Leif Bolzmann	2	2	11	6	0.14
2453	Lisa Thornton	2	2	11	6	0.14
2454	Alexandra Sauer-Ditschlerin	2	2	22	3	0.14
2455	Andrew Savage	2	2	22	3	0.14
2456	Heinz-Joachim Anders	2	2	22	3	0.14
2457	Rita Hänel	2	2	22	3	0.14
2458	Rosalia Bloch	2	2	22	3	0.14
2459	Ryan Kala	2	2	22	3	0.14
2460	Sandra Peterson	2	2	22	3	0.14
2461	Sharon Delgado	2	2	22	3	0.14
2462	Yan Zhong	2	2	22	3	0.14
2463	Chad Livingston	1	1	44	6	0.14
2464	Deanna Haney	1	1	44	6	0.14
2465	Anthony Davis	2	2	29	1	0.14
2466	Amalia Tartaglia	3	2	9	2	0.14
2467	Guiying Han	2	2	25	2	0.14
2468	Kendra Miller	2	2	25	2	0.14
2469	Yan Wan	2	2	25	2	0.14
2470	Genevieve Bell	4	2	0	0	0.14
2471	Solace Vesper	4	2	0	0	0.14
2472	Wei Xu	2	2	21	3	0.14
2473	Xia Shao	2	2	21	3	0.14
2474	Andrew Montgomery	3	2	12	1	0.14
2475	Chao Duan	3	2	12	1	0.14
2476	Ashley Patton	2	2	17	4	0.14
2477	Jessica Holmes	2	2	17	4	0.14
2478	Kylie Clarke	2	2	17	4	0.14
2479	Natasha Mcdaniel	2	2	17	4	0.14
2480	Xia Duan	2	2	17	4	0.14
2481	Xiulan Xu	2	2	17	4	0.14
2482	Victoria Williams	2	1	34	4	0.14
2483	Andrew Snyder	3	2	8	2	0.14
2484	Frédéric de la Coulon	3	2	8	2	0.14
2485	Valérie Mathieu	3	2	8	2	0.14
2486	Jasper Quinn	5	1	0	0	0.14
2487	Joe Perry	2	2	24	2	0.14
2488	Ming Pan	2	2	24	2	0.14
2489	Shannon Davis	2	2	24	2	0.14
2490	Xia Zhong	2	2	24	2	0.14
2491	Ashley Hanson	1	1	35	8	0.14
2492	Candice Torres	2	2	20	3	0.14
2493	Jun Mo	2	2	20	3	0.14
2494	Kimberly Smith	2	2	20	3	0.14
2495	Maurits van Bunschoten	2	2	20	3	0.14
2496	Paulo Oestrovsky	2	2	20	3	0.14
2497	Ping Shi	2	2	20	3	0.14
2498	Qiang Li	2	2	20	3	0.14
2499	Yong Gu	2	2	20	3	0.14
2500	Ping Kong	1	1	31	9	0.14
2501	Chao Xiang	2	2	16	4	0.14
2502	Gang Fu	2	2	16	4	0.14
2503	Mannat Johal	2	2	16	4	0.14
2504	Simone Säuberlich	2	2	16	4	0.14
2505	Yang Zhu	2	2	27	1	0.14
2506	Danny Williams	1	1	27	10	0.14
2507	Jennifer Hunt	1	1	27	10	0.14
2508	Katherine Sullivan	1	1	27	10	0.14
2509	Nicole Flynn	1	1	27	10	0.14
2510	Rachel Jones	1	1	27	10	0.14
2511	Amanda Nelson	2	2	12	5	0.14
2512	Chantal Stadelmann	2	2	12	5	0.14
2513	Dörthe Dehmel	2	2	12	5	0.14
2514	Larry Fields	2	2	12	5	0.14
2515	Stephanie Morales	2	2	12	5	0.14
2516	Thierry Labbé	2	2	12	5	0.14
2517	Kimberly Brown	2	2	23	2	0.13
2518	Li Hou	2	2	23	2	0.13
2519	Min Qiao	2	2	23	2	0.13
2520	Nicole Godard	2	2	23	2	0.13
2521	Yakshit Toor	2	2	8	6	0.13
2522	Alphons Donati-Pederiva	2	2	19	3	0.13
2523	Anthony Frost	2	2	19	3	0.13
2524	Augusto Villaverde Armengol	2	2	19	3	0.13
2525	David Jenkins	2	2	19	3	0.13
2526	Feliciano Pizarro Moll	2	2	19	3	0.13
2527	Franco Angeli	2	2	19	3	0.13
2528	Joshua Chase	2	2	19	3	0.13
2529	Jun Shi	2	2	19	3	0.13
2530	Maria Shea	2	2	19	3	0.13
2531	Stacey Campbell	2	2	19	3	0.13
2532	Todd Maldonado	2	2	19	3	0.13
2533	Jun Yang	3	2	10	1	0.13
2534	Tao Zhou	2	2	15	4	0.13
2535	John Wilkinson	3	2	6	2	0.13
2536	Whispered Alibi	3	2	6	2	0.13
2537	Chao Cai	2	2	22	2	0.13
2538	Cody Turner	2	2	22	2	0.13
2539	Stacy Watts	2	2	22	2	0.13
2540	Julita Koziarz	1	1	33	8	0.13
2541	Sylvie Scholz	2	2	7	6	0.13
2542	Aaron Ross	2	2	18	3	0.13
2543	Birthe Täsche	2	2	18	3	0.13
2544	Charlene Randolph	2	2	18	3	0.13
2545	Citrus Spirit	2	2	18	3	0.13
2546	Dolores Pardo Bárcena	2	2	18	3	0.13
2547	Fredy Lange	2	2	18	3	0.13
2548	Herr Oliver Schacht	2	2	18	3	0.13
2549	Joris Lieshout	2	2	18	3	0.13
2550	Michael Norman	2	2	18	3	0.13
2551	Pamela Roberts	2	2	18	3	0.13
2552	Pierluigi Telesio	2	2	18	3	0.13
2553	Richard Page	2	2	18	3	0.13
2554	Rubén Medina-Casas	2	2	18	3	0.13
2555	Salomón Miró Olivé	2	2	18	3	0.13
2556	Sigfrido Manu Morcillo Camino	2	2	18	3	0.13
2557	Torquato Fantini	2	2	18	3	0.13
2558	Vittorio Tamburello	2	2	18	3	0.13
2559	Paul Sanders	2	2	14	4	0.13
2560	Thomas Brown	2	2	25	1	0.13
2561	Gang Cheng	1	1	36	7	0.13
2562	Ewa Zwara	2	2	10	5	0.13
2563	Gang Zhu	2	2	21	2	0.13
2564	Matthew Brown	2	2	21	2	0.13
2565	Min Yuan	2	2	21	2	0.13
2566	Ming Yu	2	2	21	2	0.13
2567	Timothy Elliott	2	2	21	2	0.13
2568	Yan Bai	2	2	21	2	0.13
2569	Hans-Eberhard Schleich	2	1	27	5	0.13
2570	Tino Ring	1	1	32	8	0.13
2571	Daniel Mitchell	3	2	12	0	0.13
2572	Yan Cheng	3	2	12	0	0.13
2573	Crystal Sanders	2	2	17	3	0.13
2574	Ricardo Collins	2	2	17	3	0.13
2575	Tao Qian	2	2	17	3	0.13
2576	Anne Cobb	1	1	39	6	0.13
2577	Brandon Simmons	1	1	39	6	0.13
2578	Danielle Wright	1	1	39	6	0.13
2579	Lindsey Walker	1	1	39	6	0.13
2580	Tammy Norris	1	1	39	6	0.13
2581	Xiulan Song	2	2	24	1	0.13
2582	Eric Holland	1	1	35	7	0.13
2583	John Reed	1	1	35	7	0.13
2584	Kyle Smith	1	1	35	7	0.13
2585	Randy Gonzalez	1	1	35	7	0.13
2586	Ryan Woodard	1	1	35	7	0.13
2587	Caroline King	2	2	9	5	0.13
2588	Lindsay Nichols	2	2	9	5	0.13
2589	Moonlit Avenue	2	2	9	5	0.13
2590	Arsenio Saraceno-Mercati	2	2	20	2	0.13
2591	Chao Zhou	2	2	20	2	0.13
2592	Esther Schinke	2	2	20	2	0.13
2593	Fang Yao	2	2	20	2	0.13
2594	Gregor Dippel	2	2	20	2	0.13
2595	Yang Zhou	3	2	11	0	0.13
2596	Elżbieta Bociek	2	2	5	6	0.13
2597	Leah Burns	2	2	16	3	0.13
2598	Jordan Mullins	2	1	22	6	0.13
2599	Timothy Shea	2	1	22	6	0.13
2600	Christopher Martinez	1	1	27	9	0.13
2601	Russell Blake	1	1	27	9	0.13
2602	Katherine Francis	1	1	38	6	0.13
2603	Ingeborg Ullrich	2	2	12	4	0.13
2604	Jacob Brown	2	2	12	4	0.13
2605	Jonathan Wyatt	2	2	12	4	0.13
2606	Justin Doyle	2	2	12	4	0.13
2607	Sonia Suda	2	2	12	4	0.13
2608	Kari Williams	1	1	34	7	0.13
2609	Richard Morton	1	1	34	7	0.13
2610	Jessica Stewart	3	2	3	2	0.13
2611	Cheryl Waters	2	2	19	2	0.13
2612	Heather Moss	2	2	19	2	0.13
2613	Paul Bailey	2	2	19	2	0.13
2614	Qiang Wei	2	2	19	2	0.13
2615	Yong Du	2	2	19	2	0.13
2616	Amanda Smith	1	1	30	8	0.13
2617	Jonathan Hayes	1	1	30	8	0.13
2618	Terri Cortez	1	1	30	8	0.13
2619	Bibi Vos	1	1	41	5	0.13
2620	Cato van Holland	1	1	41	5	0.13
2621	Femke Mulder	1	1	41	5	0.13
2622	Femke van Dagsburg	1	1	41	5	0.13
2623	Jesper Heijmans	1	1	41	5	0.13
2624	Julie Schatteleijn-Haack	1	1	41	5	0.13
2625	Julie van der Noot	1	1	41	5	0.13
2626	Mark Hellevoort-Verbruggen	1	1	41	5	0.13
2627	Mats Bökenkamp	1	1	41	5	0.13
2628	Piotr Smoter	1	1	41	5	0.13
2629	Sofie van der Hoeven	1	1	41	5	0.13
2630	Szymon Samol	1	1	41	5	0.13
2631	Courtney Carroll	2	2	15	3	0.13
2632	Hans Georg Eigenwillig-Reising	2	2	15	3	0.13
2633	Kelly Munoz	2	2	15	3	0.13
2634	Min Hao	2	2	15	3	0.13
2635	Edward Morgan	2	1	21	6	0.13
2636	Alejandra Gould	1	1	15	12	0.13
2637	John Dean	1	1	15	12	0.13
2638	Jie Qiao	2	2	26	0	0.13
2639	Sherry Russell	2	2	11	4	0.13
2640	Tammy Mcdonald	2	2	11	4	0.13
2641	Guiying Fang	2	2	22	1	0.13
2642	Diya Swamy	3	2	2	2	0.13
2643	Christina Mendoza	2	2	7	5	0.13
2644	Candela Pallarès Artigas	2	2	18	2	0.13
2645	Fang Lei	2	2	18	2	0.13
2646	Gang Han	2	2	18	2	0.13
2647	Li Lai	2	2	18	2	0.13
2648	Miss Amy Harmon	2	2	18	2	0.13
2649	Qiang Zhu	2	2	18	2	0.13
2650	Ricarda Nélida Palmer Serra	2	2	18	2	0.13
2651	Yan Ding	2	2	18	2	0.13
2652	Aniela Mijas	1	1	18	11	0.13
2653	Agnès de Leroy	1	1	29	8	0.13
2654	Gang Jiang	2	2	14	3	0.13
2655	Kimberly Nelson	2	2	14	3	0.13
2656	Stephanie Montoya	2	2	14	3	0.13
2657	Apolonia Chlebda	1	1	14	12	0.13
2658	Mariner's Refrain	4	1	6	1	0.13
2659	David Franklin	3	2	5	1	0.13
2660	Desert Echoes	3	2	5	1	0.13
2661	Cameron Faulkner	2	2	10	4	0.13
2662	Chao Hao	2	2	10	4	0.13
2663	Peripheral Circuit	2	2	10	4	0.13
2664	Pivotal Trinity	2	2	10	4	0.13
2665	The Feast	2	2	10	4	0.13
2666	Timothy Lewis	2	2	10	4	0.13
2667	Jason Evans	2	2	21	1	0.13
2668	Courtney Phillips	1	1	32	7	0.13
2669	Kelli Turner	1	1	32	7	0.13
2670	Latasha Chavez	1	1	32	7	0.13
2671	Giacinto Maderna	2	2	6	5	0.13
2672	Janos Zimmer	2	2	6	5	0.13
2673	Krzysztof Linda	2	2	6	5	0.13
2674	Beppe Agazzi-Muratori	2	2	17	2	0.12
2675	Hans-Theo Eckbauer-Dehmel	2	2	17	2	0.12
2676	Lei Zhang	2	2	17	2	0.12
2677	Ming Feng	2	2	17	2	0.12
2678	Na Qiu	2	2	17	2	0.12
2679	Xia Gu	2	2	17	2	0.12
2680	Xia Shen	2	2	17	2	0.12
2681	Yang Lu	2	2	17	2	0.12
2682	Robert Peters	1	1	28	8	0.12
2683	Brenda Dunn	2	2	13	3	0.12
2684	Cosmic Sankey	2	2	13	3	0.12
2685	Cristina Williams	2	2	13	3	0.12
2686	Emily Glenn	2	2	13	3	0.12
2687	Ewa Kucz	2	2	13	3	0.12
2688	Jacqueline Monroe	2	2	13	3	0.12
2689	Jennifer Collins	2	2	13	3	0.12
2690	Mark Sparks	2	2	13	3	0.12
2691	Norma Huff	2	1	30	3	0.12
2692	Asuka Fukuda	2	2	9	4	0.12
2693	Crystal Norman	2	2	20	1	0.12
2694	Justin Campbell	2	2	20	1	0.12
2695	Mark Nichols	2	2	20	1	0.12
2696	Xia Qian	2	2	20	1	0.12
2697	Xiulan Lei	2	2	20	1	0.12
2698	Matthew Smith	2	1	26	4	0.12
2699	Hortense du Dijoux	2	2	16	2	0.12
2700	Jun Kong	2	2	16	2	0.12
2701	Kunigunde Pechel	2	2	16	2	0.12
2702	Li Kang	2	2	16	2	0.12
2703	Li Liu	2	2	16	2	0.12
2704	Melinda Gregory	2	2	16	2	0.12
2705	Na Shi	2	2	16	2	0.12
2706	Nicholas Chen	2	2	16	2	0.12
2707	Stacey Christensen	2	2	16	2	0.12
2708	Vincent Seifert	2	2	16	2	0.12
2709	Wei Xue	2	2	16	2	0.12
2710	Xia Su	2	2	16	2	0.12
2711	Yang Meng	2	2	16	2	0.12
2712	Erica Hernandez	1	1	27	8	0.12
2713	Jules Paris	1	1	27	8	0.12
2714	Kathy Johnson	1	1	27	8	0.12
2715	Lee Butler	1	1	27	8	0.12
2716	Livia Blewanus	1	1	27	8	0.12
2717	Antonio Johnson	2	2	12	3	0.12
2718	Brooke Olson	2	2	12	3	0.12
2719	Dina Borroni	2	2	12	3	0.12
2720	Jing Jia	2	2	12	3	0.12
2721	Joseph Figueroa	2	2	12	3	0.12
2722	Matilda Zarlino	2	2	12	3	0.12
2723	Pierangelo Papafava	2	2	12	3	0.12
2724	Silver Seraph	2	2	12	3	0.12
2725	Victoria Higgins	2	2	12	3	0.12
2726	Heather Wood	1	1	34	6	0.12
2727	Laura Jefferson	1	1	34	6	0.12
2728	Hansh Ramanathan	3	2	3	1	0.12
2729	Gregory Reeves	2	2	19	1	0.12
2730	Leo Schuster	2	2	19	1	0.12
2731	Qiang Jiang	2	2	19	1	0.12
2732	Yang Su	2	2	19	1	0.12
2733	Eric Reyes	1	1	30	7	0.12
2734	Monica Franco	1	1	30	7	0.12
2735	Sharon Gallegos	1	1	30	7	0.12
2736	Gang Chang	2	2	15	2	0.12
2737	Min Zhong	2	2	15	2	0.12
2738	Morgan Hernandez	2	2	15	2	0.12
2739	Patricia Pope	2	2	15	2	0.12
2740	Ping Liang	2	2	15	2	0.12
2741	Tao Xiang	2	2	15	2	0.12
2742	Xiuying Mao	2	2	15	2	0.12
2743	Yang Cai	2	2	15	2	0.12
2744	Yang Yin	2	2	15	2	0.12
2745	Yong Chang	2	2	15	2	0.12
2746	Michael Warner	1	1	26	8	0.12
2747	Onkar Chacko	1	1	26	8	0.12
2748	Robert Long	1	1	26	8	0.12
2749	Aaron Logan	1	1	37	5	0.12
2750	Carol Hanna	1	1	37	5	0.12
2751	Kevin Kidd	1	1	37	5	0.12
2752	Megan Dunn	1	1	37	5	0.12
2753	William Moore	1	1	37	5	0.12
2754	Andrew Lang	2	2	11	3	0.12
2755	Hanne-Lore Scholl-Heser	2	2	11	3	0.12
2756	Hildegund Wilmsen	2	2	11	3	0.12
2757	Jutta Matthäi	2	2	11	3	0.12
2758	Laetitia Petitjean	2	2	11	3	0.12
2759	Madeleine Pineau	2	2	11	3	0.12
2760	Qiang Wang	2	2	11	3	0.12
2761	Rosalie Hettner-Warmer	2	2	11	3	0.12
2762	Susan Da Silva	2	2	11	3	0.12
2763	Xia Xiang	2	2	11	3	0.12
2764	Yesenia Tyler	2	2	11	3	0.12
2765	Émilie Maillet	2	2	11	3	0.12
2766	Carl Osborne	2	2	22	0	0.12
2767	Maurice Mathieu	2	1	28	3	0.12
2768	Emily Jackson	1	1	33	6	0.12
2769	Na Wan	1	0	39	9	0.12
2770	Beverly Thompson	2	2	7	4	0.12
2771	Jennifer Hart	2	2	18	1	0.12
2772	Min Ma	2	2	18	1	0.12
2773	Benjamin Mercer	2	2	14	2	0.12
2774	Chao Cao	2	2	14	2	0.12
2775	Hansjürgen Pechel	2	2	14	2	0.12
2776	Jie Xia	2	2	14	2	0.12
2777	Lei Liu	2	2	14	2	0.12
2778	Lilian Gieß	2	2	14	2	0.12
2779	Mario Stumpf	2	2	14	2	0.12
2780	Marvin Holzapfel	2	2	14	2	0.12
2781	Ming Tang	2	2	14	2	0.12
2782	Qiang Zou	2	2	14	2	0.12
2783	Tao Ma	2	2	14	2	0.12
2784	John Bradley	1	1	25	8	0.12
2785	Kristina Allen	1	1	25	8	0.12
2786	Stephanie Fritz	1	1	25	8	0.12
2787	Emilie Rudolph	2	2	10	3	0.12
2788	Guiying Xiang	2	2	10	3	0.12
2789	Marlene Rose	2	2	10	3	0.12
2790	Robert Nunez	2	2	10	3	0.12
2791	Tammy Fields	2	2	10	3	0.12
2792	Warm Circuit	2	2	10	3	0.12
2793	Jing He	2	2	21	0	0.12
2794	Marcel Camus-Meunier	2	2	6	4	0.12
2795	Chao Luo	2	2	17	1	0.12
2796	Jie Sun	2	2	17	1	0.12
2797	Li Yuan	2	2	17	1	0.12
2798	Yang Qiu	2	2	17	1	0.12
2799	Andrzej Schottin-Mühle	2	2	13	2	0.12
2800	Chao Xue	2	2	13	2	0.12
2801	Daria Boucsein	2	2	13	2	0.12
2802	David Williams	2	2	13	2	0.12
2803	Gang Li	2	2	13	2	0.12
2804	Jean Buisson	2	2	13	2	0.12
2805	Kayla Campbell	2	2	13	2	0.12
2806	Lei Yu	2	2	13	2	0.12
2807	Min Cheng	2	2	13	2	0.12
2808	Na Yu	2	2	13	2	0.12
2809	Orhan Krein	2	2	13	2	0.12
2810	Shawn Wilson	2	2	13	2	0.12
2811	Sophia Baracca-Solimena	2	2	13	2	0.12
2812	Vincentio Galiazzo	2	2	13	2	0.12
2813	Eliza Brooks	3	2	4	0	0.12
2814	Imelda Zaguri	3	2	4	0	0.12
2815	Jasper Reed	3	2	4	0	0.12
2816	Nanni Telesio	3	2	4	0	0.12
2817	Arkadiusz Flisiak	2	2	9	3	0.12
2818	Dusan Ullmann	2	2	9	3	0.12
2819	Indranil Vala	2	2	9	3	0.12
2820	Kashvi Keer	2	2	9	3	0.12
2821	Lenn Blaak	2	2	9	3	0.12
2822	Los Leones del Valle	2	2	20	0	0.12
2823	Xia Yin	2	2	20	0	0.12
2824	Lisa Steele	1	1	31	6	0.12
2825	Elizabeth Shepherd	2	2	5	4	0.12
2826	Alma Davids	2	2	16	1	0.12
2827	Christopher Hill	2	2	16	1	0.12
2828	Guiying Liang	2	2	16	1	0.12
2829	Guiying Ye	2	2	16	1	0.12
2830	Nicole Simmons	2	2	16	1	0.12
2831	Tao Lai	2	2	16	1	0.12
2832	Yang Lin	2	2	16	1	0.12
2833	Amanda Tucker	2	2	12	2	0.12
2834	Anthony Hancock	2	2	12	2	0.12
2835	Cipriano Peranda	2	2	12	2	0.12
2836	Colleen Frank PhD	2	2	12	2	0.12
2837	Deanna Hines	2	2	12	2	0.12
2838	Henri Drubin	2	2	12	2	0.12
2839	Kristin Floyd	2	2	12	2	0.12
2840	Kristina Romero	2	2	12	2	0.12
2841	Min Zeng	2	2	12	2	0.12
2842	Nicholas Garcia	2	2	12	2	0.12
2843	Xia Meng	2	2	12	2	0.12
2844	Yan Shao	2	2	12	2	0.12
2845	Nadia van Dooren-de Jode Vastraedsd	2	1	29	2	0.12
2846	Christopher Hernandez	1	1	23	8	0.12
2847	Deborah Hernandez	1	1	34	5	0.12
2848	Gregory Thompson	1	1	34	5	0.12
2849	Hiroshi Shimizu	1	1	34	5	0.12
2850	Adam Nelson	2	2	8	3	0.12
2851	Anthony Cox	2	2	8	3	0.12
2852	Anto Hornich	2	2	8	3	0.12
2853	David Silva	2	2	8	3	0.12
2854	Elizabeth Nguyen	2	2	8	3	0.12
2855	Imelda Staude-Trupp	2	2	8	3	0.12
2856	James Green	2	2	8	3	0.12
2857	Kelly Brown	2	2	8	3	0.12
2858	Michael Hartman	2	2	8	3	0.12
2859	Steven Fox	2	2	8	3	0.12
2860	Tadeusz Langern	2	2	8	3	0.12
2861	Yang Li	2	2	19	0	0.12
2862	James Clark	1	1	30	6	0.12
2863	Chao Liang	2	2	15	1	0.12
2864	Evamaria Geisel	2	2	15	1	0.12
2865	Gang Liao	2	2	15	1	0.12
2866	Gang Qiao	2	2	15	1	0.12
2867	Guiying Kong	2	2	15	1	0.12
2868	Jing Cao	2	2	15	1	0.12
2869	Jing Ma	2	2	15	1	0.12
2870	Karen Williams	2	2	15	1	0.12
2871	Lei Guo	2	2	15	1	0.12
2872	Ping Ma	2	2	15	1	0.12
2873	Ping Xue	2	2	15	1	0.12
2874	Sonic Disintegration Protocol	2	2	15	1	0.12
2875	Xia Wen	2	2	15	1	0.12
2876	Yan Cao	2	2	15	1	0.12
2877	Georgina Piñol-Guerra	1	0	43	7	0.12
2878	Lalo Salgado Cazorla	1	0	43	7	0.12
2879	Luigina Stoppani-Sandi	1	0	43	7	0.12
2880	Arhaan Batta	2	2	11	2	0.11
2881	Billy Tyler	2	2	11	2	0.11
2882	Chao Mao	2	2	11	2	0.11
2883	Electric Twist	2	2	11	2	0.11
2884	Ida Ryfa	2	2	11	2	0.11
2885	Ivory Pulse	2	2	11	2	0.11
2886	Klara Parkitna	2	2	11	2	0.11
2887	Michael Jones	2	2	11	2	0.11
2888	Na Tang	2	2	11	2	0.11
2889	Nicholas Riddle	2	2	11	2	0.11
2890	Sarah Fernandez	2	2	11	2	0.11
2891	The Midnight Sentinels	2	2	11	2	0.11
2892	The Lunar Tides	4	1	3	0	0.11
2893	Jared Mercer	1	1	33	5	0.11
2894	Kim Knight	1	1	33	5	0.11
2895	Cilly Säuberlich	2	2	7	3	0.11
2896	Ian Foster	2	2	7	3	0.11
2897	Mehdi Hering	2	2	7	3	0.11
2898	Reinhild Krein	2	2	7	3	0.11
2899	The Chaotic Realm of Elliot Flame	2	2	7	3	0.11
2900	Tillmann Klemm	2	2	7	3	0.11
2901	Chao Hou	2	2	14	1	0.11
2902	Chao Shao	2	2	14	1	0.11
2903	Daniel Johnson	2	2	14	1	0.11
2904	Guiying Xu	2	2	14	1	0.11
2905	Kristi Bowers	2	2	14	1	0.11
2906	Lei Zou	2	2	14	1	0.11
2907	Min Zheng	2	2	14	1	0.11
2908	Qiang Hao	2	2	14	1	0.11
2909	Qiang Zheng	2	2	14	1	0.11
2910	Thomas Ball	2	2	14	1	0.11
2911	Xia Liao	2	2	14	1	0.11
2912	Xia Xu	2	2	14	1	0.11
2913	Xiuying Zou	2	2	14	1	0.11
2914	Yong Wu	2	2	14	1	0.11
2915	Zaira Balotelli-Peano	2	2	14	1	0.11
2916	Akemi Sato	2	2	10	2	0.11
2917	Duuk Schokman	2	2	10	2	0.11
2918	Fang Deng	2	2	10	2	0.11
2919	Gioele Monte	2	2	10	2	0.11
2920	Hiroshi Nakamura	2	2	10	2	0.11
2921	Juan Hao	2	2	10	2	0.11
2922	Jurre Kunen	2	2	10	2	0.11
2923	Karl-Friedrich auch Schlauchin	2	2	10	2	0.11
2924	Kenan Reichmann	2	2	10	2	0.11
2925	Lauretta Pasolini	2	2	10	2	0.11
2926	Linda Santos	2	2	10	2	0.11
2927	Lukas Waltrade Walderade-van Dam	2	2	10	2	0.11
2928	Marcin Maksimowicz	2	2	10	2	0.11
2929	Melodic Mischief	2	2	10	2	0.11
2930	Mila Hellevoort-Robbrechts Bruijne	2	2	10	2	0.11
2931	Rei Hayashi	2	2	10	2	0.11
2932	Sotaro Sato	2	2	10	2	0.11
2933	Tao Cheng	2	2	10	2	0.11
2934	Yasuhiro Yamamoto	2	2	10	2	0.11
2935	Yosuke Yamamoto	2	2	10	2	0.11
2936	Jerry Johnson	2	2	6	3	0.11
2937	Julian Biegała	2	2	6	3	0.11
2938	Gang Ren	2	2	17	0	0.11
2939	Juan Ren	2	2	17	0	0.11
2940	Falko Mielcarek	1	1	17	9	0.11
2941	Brooke Chang	2	2	13	1	0.11
2942	Carolyn Salas	2	2	13	1	0.11
2943	Guiying Deng	2	2	13	1	0.11
2944	Li Ding	2	2	13	1	0.11
2945	Min Lu	2	2	13	1	0.11
2946	Nancy Smith	2	2	13	1	0.11
2947	Qiang Liang	2	2	13	1	0.11
2948	Qiang Tian	2	2	13	1	0.11
2949	Wei Xie	2	2	13	1	0.11
2950	Xiulan Qiu	2	2	13	1	0.11
2951	Alexander Parsons	2	2	9	2	0.11
2952	Amanda Marquez	2	2	9	2	0.11
2953	Andrea Smith	2	2	9	2	0.11
2954	Charlene Lopez	2	2	9	2	0.11
2955	Frederick Freeman	2	2	9	2	0.11
2956	Jason Underwood	2	2	9	2	0.11
2957	Martin Johnson	2	2	9	2	0.11
2958	Michael Craig	2	2	9	2	0.11
2959	Ping Lin	2	2	9	2	0.11
2960	Daniel Mccormick	1	1	31	5	0.11
2961	Gabriel Gaudin	1	1	31	5	0.11
2962	Liberto Antonacci	1	1	31	5	0.11
2963	Valeria Perini	1	1	31	5	0.11
2964	Brittany Mcguire	2	2	5	3	0.11
2965	Tamara Brown	1	1	27	6	0.11
2966	Chao Ren	2	2	12	1	0.11
2967	Fang He	2	2	12	1	0.11
2968	Guiying Feng	2	2	12	1	0.11
2969	Jennifer Mccoy	2	2	12	1	0.11
2970	Jie Lin	2	2	12	1	0.11
2971	Jie Ye	2	2	12	1	0.11
2972	Jing Qiu	2	2	12	1	0.11
2973	Melissa Garcia	2	2	12	1	0.11
2974	Min Wei	2	2	12	1	0.11
2975	Ming Xie	2	2	12	1	0.11
2976	Ping Han	2	2	12	1	0.11
2977	Richard Davis	2	2	12	1	0.11
2978	Rosaria Ferrara	2	2	12	1	0.11
2979	Tao Chen	2	2	12	1	0.11
2980	Valentina Ungaretti-Pucci	2	2	12	1	0.11
2981	Xiuying Lu	2	2	12	1	0.11
2982	Yan Hu	2	2	12	1	0.11
2983	Levi Stewart	1	1	34	4	0.11
2984	Lori Delgado	1	1	34	4	0.11
2985	Whispered Resonance	3	1	9	2	0.11
2986	Amanda Morales	2	2	8	2	0.11
2987	April Brown	2	2	8	2	0.11
2988	Barbara Moore	2	2	8	2	0.11
2989	Bryan Richardson	2	2	8	2	0.11
2990	Charo Perez Castillo	2	2	8	2	0.11
2991	Daniel Collins	2	2	8	2	0.11
2992	Danny Craig	2	2	8	2	0.11
2993	Frédérique Peron	2	2	8	2	0.11
2994	Haruka Nishimura	2	2	8	2	0.11
2995	Midnight Feather	2	2	8	2	0.11
2996	Philippe Franke	2	2	8	2	0.11
2997	Shohei Ito	2	2	8	2	0.11
2998	The Rhythm Rascals	2	2	8	2	0.11
2999	Ashen Valor	5	0	1	0	0.11
3000	Amy Mack	2	1	25	2	0.11
3001	Joann Cunningham	2	1	25	2	0.11
3002	Mark Crawford	1	1	19	8	0.11
3003	Alex Thomas	2	2	4	3	0.11
3004	Amber Watson	2	2	4	3	0.11
3005	Julie Thompson	2	2	4	3	0.11
3006	Laura Lee	2	2	4	3	0.11
3007	Dolores Tasso	2	2	15	0	0.11
3008	Mark Trujillo	2	2	15	0	0.11
3009	Na Hao	2	2	15	0	0.11
3010	Renzo Dallapé	2	2	15	0	0.11
3011	Cory Wall	2	2	11	1	0.11
3012	Destiny Norman	2	2	11	1	0.11
3013	Dominique Ramos	2	2	11	1	0.11
3014	Gang Yi	2	2	11	1	0.11
3015	Jeffrey Thompson	2	2	11	1	0.11
3016	Jing Su	2	2	11	1	0.11
3017	Juan Hou	2	2	11	1	0.11
3018	Juan Luo	2	2	11	1	0.11
3019	Jun Fan	2	2	11	1	0.11
3020	Kevin Morrow	2	2	11	1	0.11
3021	Min Pan	2	2	11	1	0.11
3022	Na Fu	2	2	11	1	0.11
3023	Qiang Xiang	2	2	11	1	0.11
3024	Qiang Yan	2	2	11	1	0.11
3025	Tao Shao	2	2	11	1	0.11
3026	Xiuying Wan	2	2	11	1	0.11
3027	Yong Kong	2	2	11	1	0.11
3028	Annaliese Bolnbach	2	2	7	2	0.11
3029	Arne Baum	2	2	7	2	0.11
3030	Claudia Lee	2	2	7	2	0.11
3031	Hans-Gerhard Kuhl	2	2	7	2	0.11
3032	Hinrich Dörschner	2	2	7	2	0.11
3033	Jens-Peter Mosemann	2	2	7	2	0.11
3034	Michael Reyes	2	2	7	2	0.11
3035	Michael Rivera	2	2	7	2	0.11
3036	Tymon Miszta	2	2	7	2	0.11
3037	Crystal Morse	1	1	18	8	0.11
3038	Erik Tucker	1	1	18	8	0.11
3039	Juan Qian	2	2	14	0	0.11
3040	Sean Mcconnell	2	2	14	0	0.11
3041	Wei Fan	2	2	14	0	0.11
3042	Bruce Long	2	2	10	1	0.11
3043	Curtis Doyle	2	2	10	1	0.11
3044	Cynthia Monroe	2	2	10	1	0.11
3045	Céline Guérin	2	2	10	1	0.11
3046	Debbie Mcgrath	2	2	10	1	0.11
3047	Diana Elliott	2	2	10	1	0.11
3048	Dorota Cierpka	2	2	10	1	0.11
3049	François Coste-Daniel	2	2	10	1	0.11
3050	Gail Nguyen	2	2	10	1	0.11
3051	Hanako Yamaguchi	2	2	10	1	0.11
3052	Jamie Roberts	2	2	10	1	0.11
3053	Jeffrey Medina	2	2	10	1	0.11
3054	Jing Liang	2	2	10	1	0.11
3055	Joy Blanchard	2	2	10	1	0.11
3056	Juan Deng	2	2	10	1	0.11
3057	Li Xiong	2	2	10	1	0.11
3058	Margaux Mathieu	2	2	10	1	0.11
3059	Mary Barnes	2	2	10	1	0.11
3060	Melissa Williams	2	2	10	1	0.11
3061	Ming Lin	2	2	10	1	0.11
3062	Noah Frazier Jr.	2	2	10	1	0.11
3063	Olav Mülichen	2	2	10	1	0.11
3064	Stephanie Horton	2	2	10	1	0.11
3065	Theresa Lopez	2	2	10	1	0.11
3066	Yan Ye	2	2	10	1	0.11
3067	Élisabeth Lemoine	2	2	10	1	0.11
3068	Adam Carpenter	2	2	6	2	0.11
3069	Adam Lopez	2	2	6	2	0.11
3070	Alex Parrish	2	2	6	2	0.11
3071	Andrea Crawford	2	2	6	2	0.11
3072	Anthony Salas	2	2	6	2	0.11
3073	Benvenuto Lerner	2	2	6	2	0.11
3074	Cecilia Cusano-Squarcione	2	2	6	2	0.11
3075	Daniel Nelson	2	2	6	2	0.11
3076	Donald Davis	2	2	6	2	0.11
3077	Emily Brooks	2	2	6	2	0.11
3078	Eric Wright	2	2	6	2	0.11
3079	Frank Bonilla	2	2	6	2	0.11
3080	Gianpaolo Sibilia	2	2	6	2	0.11
3081	Giovanni Sauer	2	2	6	2	0.11
3082	Guido Dobes	2	2	6	2	0.11
3083	Holger Hiller	2	2	6	2	0.11
3084	Jennifer Smith	2	2	6	2	0.11
3085	Kristi Yu	2	2	6	2	0.11
3086	Kyle Anderson	2	2	6	2	0.11
3087	Nicole Williamson	2	2	6	2	0.11
3088	Phillip Hampton	2	2	6	2	0.11
3089	Richard Obrien	2	2	6	2	0.11
3090	Shelby Kelly	2	2	6	2	0.11
3091	Wally Beckmann	2	2	6	2	0.11
3092	Wesley Walker	2	2	6	2	0.11
3093	Chao Deng	1	1	28	5	0.11
3094	Grace Duncan	1	1	28	5	0.11
3095	Ming Fu	1	1	28	5	0.11
3096	Stephanie Wilkerson	1	1	28	5	0.11
3097	Ashley Guzman	2	2	13	0	0.11
3098	Guiying Wu	2	2	13	0	0.11
3099	Jie Ren	2	2	13	0	0.11
3100	Qiang Long	2	1	19	3	0.11
3101	Adam de Vries	1	1	24	6	0.11
3102	Ana Gonzales	1	1	24	6	0.11
3103	Aylin Sire	1	1	24	6	0.11
3104	Calogero Mezzetta	1	1	24	6	0.11
3105	Luca Hazenveld	1	1	24	6	0.11
3106	Mackenzie Brown	1	1	24	6	0.11
3107	Victoria Fletcher	1	1	24	6	0.11
3108	Alois Jäntsch-Hecker	2	2	9	1	0.11
3109	Amber Huff	2	2	9	1	0.11
3110	Chao Dong	2	2	9	1	0.11
3111	Dana Ferrell	2	2	9	1	0.11
3112	Elizabeth Griffin	2	2	9	1	0.11
3113	Jing Gu	2	2	9	1	0.11
3114	Kristi Henry	2	2	9	1	0.11
3115	Margrit Hiller-Jopich	2	2	9	1	0.11
3116	Melissa Wheeler	2	2	9	1	0.11
3117	Min Meng	2	2	9	1	0.11
3118	Ming Liu	2	2	9	1	0.11
3119	Morgan Collins	2	2	9	1	0.11
3120	Na Duan	2	2	9	1	0.11
3121	Nicolaus Fiebig	2	2	9	1	0.11
3122	Qiang Zhong	2	2	9	1	0.11
3123	Roger Simmons	2	2	9	1	0.11
3124	Sherry Powell	2	2	9	1	0.11
3125	Thomas King	2	2	9	1	0.11
3126	Wei Shao	2	2	9	1	0.11
3127	Yan Qiao	2	2	9	1	0.11
3128	Yuki Saito	2	2	9	1	0.11
3129	Adrien Techer du Voisin	2	2	5	2	0.10
3130	Barbara Smith	2	2	5	2	0.10
3131	Bouncy Gong	2	2	5	2	0.10
3132	Breanna Wright	2	2	5	2	0.10
3133	Carsten Fritsch	2	2	5	2	0.10
3134	Cheryl Delgado	2	2	5	2	0.10
3135	Christopher Powell	2	2	5	2	0.10
3136	Dawn Johnson	2	2	5	2	0.10
3137	Eugène Lemaire	2	2	5	2	0.10
3138	Franck-Georges Delattre	2	2	5	2	0.10
3139	Gang Hou	2	2	5	2	0.10
3140	Joseph Jackson	2	2	5	2	0.10
3141	Kenneth Armstrong	2	2	5	2	0.10
3142	Vittoria Mazzanti	2	2	5	2	0.10
3143	Bailey Ewing	1	1	27	5	0.10
3144	Billy Green	1	1	27	5	0.10
3145	Clayton Harper	1	1	27	5	0.10
3146	Emily Black	1	1	27	5	0.10
3147	James Price	1	1	27	5	0.10
3148	Jennifer Duarte	1	1	27	5	0.10
3149	Larry Rogers	1	1	27	5	0.10
3150	Charles Jacobs	2	2	12	0	0.10
3151	Fang Shi	2	2	12	0	0.10
3152	Juan He	2	2	12	0	0.10
3153	Lisa Shaffer	2	2	12	0	0.10
3154	Ming Han	2	2	12	0	0.10
3155	Ming Zeng	2	2	12	0	0.10
3156	Qiang Cao	2	2	12	0	0.10
3157	Qiang Qin	2	2	12	0	0.10
3158	Tao Lin	2	2	12	0	0.10
3159	Vitali Rogner	2	2	12	0	0.10
3160	Wei Zheng	2	2	12	0	0.10
3161	Xia Mao	2	2	12	0	0.10
3162	Xia Zhao	2	2	12	0	0.10
3163	Yan Yu	2	2	12	0	0.10
3164	Yong Hou	2	2	12	0	0.10
3165	Yong Qiao	2	2	12	0	0.10
3166	Andrew Morrison	2	2	8	1	0.10
3167	Billy Thompson	2	2	8	1	0.10
3168	Blazing Collective	2	2	8	1	0.10
3169	Dana Henk-Bachmann	2	2	8	1	0.10
3170	David Flores	2	2	8	1	0.10
3171	Hannah Elliott	2	2	8	1	0.10
3172	Hüseyin Paffrath	2	2	8	1	0.10
3173	James Martinez	2	2	8	1	0.10
3174	Jason Weeks	2	2	8	1	0.10
3175	Jessica Baxter	2	2	8	1	0.10
3176	Jing Lu	2	2	8	1	0.10
3177	Justin Wagner	2	2	8	1	0.10
3178	Kristiane Stadelmann	2	2	8	1	0.10
3179	Michael Aguilar	2	2	8	1	0.10
3180	Peter Goodwin	2	2	8	1	0.10
3181	Wei Lei	2	2	8	1	0.10
3182	Yang Ding	2	2	8	1	0.10
3183	Elisabet Manola Aguirre Solís	2	2	4	2	0.10
3184	Guy Martel	2	2	4	2	0.10
3185	Janet Lewis	2	2	4	2	0.10
3186	Lisa Wolfe	2	2	4	2	0.10
3187	Satomi Kobayashi	2	2	4	2	0.10
3188	Scott Flores	2	2	4	2	0.10
3189	Sharon Scott	2	2	4	2	0.10
3190	Stephen Obrien	2	2	4	2	0.10
3191	Juan Antonio Ferrando-Chico	1	1	26	5	0.10
3192	Moisés del Tirado	1	1	26	5	0.10
3193	Adah Barad	1	0	32	8	0.10
3194	Hridaan Chacko	1	0	32	8	0.10
3195	Rania Bahri	1	0	32	8	0.10
3196	Bradley Brooks	2	2	11	0	0.10
3197	Celestial Ink	2	2	11	0	0.10
3198	Fang Xu	2	2	11	0	0.10
3199	Hans-Josef Steckel-Harloff	2	2	11	0	0.10
3200	Horst-Dieter Schleich	2	2	11	0	0.10
3201	Lauren Schmitt	2	2	11	0	0.10
3202	Lei Xie	2	2	11	0	0.10
3203	Li Zhu	2	2	11	0	0.10
3204	Min Cai	2	2	11	0	0.10
3205	Xiuying Liao	2	2	11	0	0.10
3206	Yang Jin	2	2	11	0	0.10
3207	Yong Liang	2	2	11	0	0.10
3208	Jonathan Hunter	1	1	22	6	0.10
3209	Lisa Henderson	1	1	22	6	0.10
3210	Auguste-Alexandre Delattre	2	2	7	1	0.10
3211	Baccio Muti	2	2	7	1	0.10
3212	Chad Booker	2	2	7	1	0.10
3213	Christiane Langlois	2	2	7	1	0.10
3214	Franciszek Filusz	2	2	7	1	0.10
3215	Gabriel Nash	2	2	7	1	0.10
3216	Gerard Hornig	2	2	7	1	0.10
3217	Guiying Qiao	2	2	7	1	0.10
3218	Ildiko Carsten-Thies	2	2	7	1	0.10
3219	Jie Liu	2	2	7	1	0.10
3220	Krystyna Lübs	2	2	7	1	0.10
3221	Linda Guzman	2	2	7	1	0.10
3222	Marcelina Ogłaza	2	2	7	1	0.10
3223	Mary Thompson	2	2	7	1	0.10
3224	Min Lin	2	2	7	1	0.10
3225	Paul Powell	2	2	7	1	0.10
3226	Radosław Duszkiewicz	2	2	7	1	0.10
3227	Robert Norris	2	2	7	1	0.10
3228	Ulrike Ziegert	2	2	7	1	0.10
3229	Victoire Morvan	2	2	7	1	0.10
3230	Xiulan Cheng	2	2	7	1	0.10
3231	Fang Gong	1	1	29	4	0.10
3232	Jennifer Rice	1	1	29	4	0.10
3233	Jeremiah Love	2	2	3	2	0.10
3234	Kimaya Jaggi	2	2	3	2	0.10
3235	Marisa Tresoldi	2	2	3	2	0.10
3236	Miki Hasegawa	2	2	3	2	0.10
3237	Miki Miura	2	2	3	2	0.10
3238	Miłosz Staciwa	2	2	3	2	0.10
3239	Shlok Desai	2	2	3	2	0.10
3240	Vanessa Trevisani	2	2	3	2	0.10
3241	Velvet Prism	2	2	3	2	0.10
3242	Victoria Carpaccio	2	2	3	2	0.10
3243	Gang Su	2	1	20	2	0.10
3244	Jennifer Faust	1	1	25	5	0.10
3245	Wieland Käster	1	1	25	5	0.10
3246	Chao Lin	2	2	10	0	0.10
3247	Divit Srinivas	2	2	10	0	0.10
3248	Fang Xia	2	2	10	0	0.10
3249	Guiying Liu	2	2	10	0	0.10
3250	Guiying Zhao	2	2	10	0	0.10
3251	Jing Jiang	2	2	10	0	0.10
3252	Kaylee Quinn	2	2	10	0	0.10
3253	Kimberly Stewart	2	2	10	0	0.10
3254	Na Gao	2	2	10	0	0.10
3255	Na Tan	2	2	10	0	0.10
3256	Qiang Shao	2	2	10	0	0.10
3257	Tao Deng	2	2	10	0	0.10
3258	William Rodriguez	2	2	10	0	0.10
3259	Yang Dong	2	2	10	0	0.10
3260	Aaron Anderson	1	1	32	3	0.10
3261	Ashley Myers	1	1	32	3	0.10
3262	Curtis Jones	1	1	32	3	0.10
3263	David Benson	1	1	32	3	0.10
3264	Joshua Rodriguez	1	1	32	3	0.10
3265	Robert Gray	1	1	32	3	0.10
3266	William Dominguez	1	1	32	3	0.10
3267	Yang Xiong	3	1	7	1	0.10
3268	Anaya Barman	2	2	6	1	0.10
3269	Ann Nielsen	2	2	6	1	0.10
3270	Artur Garbacik	2	2	6	1	0.10
3271	Brian Contreras	2	2	6	1	0.10
3272	Christine Bartlett	2	2	6	1	0.10
3273	Colton Dickerson	2	2	6	1	0.10
3274	Damini Kata	2	2	6	1	0.10
3275	Elijah Smith	2	2	6	1	0.10
3276	Emily Nash	2	2	6	1	0.10
3277	Igor Goliasz	2	2	6	1	0.10
3278	Jivika Mahal	2	2	6	1	0.10
3279	Konrad Klinkosz	2	2	6	1	0.10
3280	Laura Beck	2	2	6	1	0.10
3281	Lei Li	2	2	6	1	0.10
3282	Lindsay Williams	2	2	6	1	0.10
3283	Margaud Morel	2	2	6	1	0.10
3284	Mary Maxwell	2	2	6	1	0.10
3285	Michelle Stafford	2	2	6	1	0.10
3286	Nicole Cook	2	2	6	1	0.10
3287	Noor van Hoevel en van Zwindrecht	2	2	6	1	0.10
3288	Patricia Myers	2	2	6	1	0.10
3289	Przemysław Szałkiewicz	2	2	6	1	0.10
3290	Richard Goodman	2	2	6	1	0.10
3291	Ryan Lin	2	2	6	1	0.10
3292	Shelly Kelly	2	2	6	1	0.10
3293	Yésica de Oller	2	2	6	1	0.10
3294	Anita Parada	1	1	17	7	0.10
3295	Larry Massey	1	1	28	4	0.10
3296	Raymond Kennedy	1	1	28	4	0.10
3297	Rene Wulff-Kranz	1	1	28	4	0.10
3298	Andrew Richard	2	2	2	2	0.10
3299	Jeremi Wita	2	2	2	2	0.10
3300	Phillip Hamilton	2	2	2	2	0.10
3301	Sonia Krochmal	2	2	2	2	0.10
3302	Sébastien Delaunay	2	2	2	2	0.10
3303	Michael Price	2	1	19	2	0.10
3304	Edwin Hansen	2	2	9	0	0.10
3305	Guiying Yang	2	2	9	0	0.10
3306	Jie Song	2	2	9	0	0.10
3307	Juan Dai	2	2	9	0	0.10
3308	Karl-Friedrich Kreusel	2	2	9	0	0.10
3309	Na Tian	2	2	9	0	0.10
3310	Na Xiang	2	2	9	0	0.10
3311	Ping Chang	2	2	9	0	0.10
3312	Wei Bai	2	2	9	0	0.10
3313	Wei Kong	2	2	9	0	0.10
3314	Xiuying Long	2	2	9	0	0.10
3315	Xiuying Shi	2	2	9	0	0.10
3316	Yan Hao	2	2	9	0	0.10
3317	Yan Ren	2	2	9	0	0.10
3318	Yang Song	2	2	9	0	0.10
3319	Ella Parker	3	1	6	1	0.10
3320	Aarav Chana	2	2	5	1	0.10
3321	Alfredo Cases Gaya	2	2	5	1	0.10
3322	Arnav Khosla	2	2	5	1	0.10
3323	Brandon Roman	2	2	5	1	0.10
3324	Carol Moyer	2	2	5	1	0.10
3325	Cengiz Knappe	2	2	5	1	0.10
3326	Cheryl Malone	2	2	5	1	0.10
3327	Christopher Long	2	2	5	1	0.10
3328	Cristina Marenzio	2	2	5	1	0.10
3329	Gregory Hartman	2	2	5	1	0.10
3330	Isa Balotelli	2	2	5	1	0.10
3331	Jason Green	2	2	5	1	0.10
3332	Jun Bai	2	2	5	1	0.10
3333	Kayla Smith	2	2	5	1	0.10
3334	Kaylee Patrick	2	2	5	1	0.10
3335	Kerri Castillo	2	2	5	1	0.10
3336	Marianna Polaszek	2	2	5	1	0.10
3337	Mary Campbell	2	2	5	1	0.10
3338	Meredith Sullivan	2	2	5	1	0.10
3339	Neon Pulse	2	2	5	1	0.10
3340	Raghav Chawla	2	2	5	1	0.10
3341	Ranbir Sangha	2	2	5	1	0.10
3342	Reyansh Bir	2	2	5	1	0.10
3343	Rouven Ernst	2	2	5	1	0.10
3344	Silent Horizon	2	2	5	1	0.10
3345	Sotaro Ito	2	2	5	1	0.10
3346	Tyler Watson	2	2	5	1	0.10
3347	Vidur D’Alia	2	2	5	1	0.10
3348	Walfried Ernst	2	2	5	1	0.10
3349	William Alexander	2	2	5	1	0.10
3350	Étienne Bruneau	2	2	5	1	0.10
3351	Xia Jiang	2	1	22	1	0.10
3352	Anthony Beck	1	1	16	7	0.10
3353	Kari Barnes	1	1	16	7	0.10
3354	Kimberly Gross	1	1	16	7	0.10
3355	Nadine Eberhardt	1	1	27	4	0.10
3356	Chao Tao	2	1	18	2	0.10
3357	Christopher Evans	1	1	23	5	0.10
3358	Emmi Biggen	1	1	23	5	0.10
3359	Johanna Hauffer	1	1	23	5	0.10
3360	Maike Trubin	1	1	23	5	0.10
3361	Carol Reid	2	2	8	0	0.10
3362	Chad Marks	2	2	8	0	0.10
3363	Erik Harris	2	2	8	0	0.10
3364	Gang Kong	2	2	8	0	0.10
3365	Jing Wu	2	2	8	0	0.10
3366	Juan Wang	2	2	8	0	0.10
3367	Liwia Piejko	2	2	8	0	0.10
3368	Nicolas Blanc	2	2	8	0	0.10
3369	Richard Rodriguez	2	2	8	0	0.10
3370	Ryohei Ito	2	2	8	0	0.10
3371	Stéphane Hernandez	2	2	8	0	0.10
3372	The Nulls	2	2	8	0	0.10
3373	Xia Xiao	2	2	8	0	0.10
3374	Yan Gu	2	2	8	0	0.10
3375	Zoé Roux	2	2	8	0	0.10
3376	Élisabeth Vasseur	2	2	8	0	0.10
3377	Adam Arnold	1	1	30	3	0.10
3378	Carlos Townsend	1	1	30	3	0.10
3379	Dustin Lopez	2	2	4	1	0.10
3380	Emily Raymond	2	2	4	1	0.10
3381	Gabriel Barr	2	2	4	1	0.10
3382	Ida Lachmann	2	2	4	1	0.10
3383	Isabell Berger	2	2	4	1	0.10
3384	Jason Brown	2	2	4	1	0.10
3385	Juan Guo	2	2	4	1	0.10
3386	Justin Thomas	2	2	4	1	0.10
3387	Kathleen Harvey	2	2	4	1	0.10
3388	Latoya English	2	2	4	1	0.10
3389	Laura Anderson	2	2	4	1	0.10
3390	Leonard Phillips	2	2	4	1	0.10
3391	Lise Gerrits	2	2	4	1	0.10
3392	Matthew Gallagher	2	2	4	1	0.10
3393	Matthew Nichols	2	2	4	1	0.10
3394	Matthew Robertson	2	2	4	1	0.10
3395	Michael French	2	2	4	1	0.10
3396	Michelle Smith	2	2	4	1	0.10
3397	Nikola Nerger	2	2	4	1	0.10
3398	Seth Duncan	2	2	4	1	0.10
3399	Siglinde Ruppersberger	2	2	4	1	0.10
3400	Walter Bell	2	2	4	1	0.10
3401	Wei Yi	2	2	4	1	0.10
3402	Yang Tan	2	2	4	1	0.10
3403	Yong Liu	2	2	4	1	0.10
3404	Brandy Reyes	1	1	26	4	0.10
3405	Dennis Wood	1	1	26	4	0.10
3406	Justin Kirk	1	1	26	4	0.10
3407	Laura Gibbs	1	1	26	4	0.10
3408	Mary Medina	1	1	26	4	0.10
3409	Pamela Miller	1	1	26	4	0.10
3410	Peter Vega	1	1	26	4	0.10
3411	Raymond Mccoy	1	1	26	4	0.10
3412	Rhonda Brown	1	1	26	4	0.10
3413	Yong Shao	2	1	17	2	0.10
3414	Gabriel Olson	1	1	22	5	0.10
3415	James Stevens	1	1	22	5	0.10
3416	Jeremy Little	1	1	22	5	0.10
3417	Yong Song	1	1	22	5	0.10
3418	Bertrand-Robert Duval	2	2	7	0	0.10
3419	Dustin Newman	2	2	7	0	0.10
3420	Edward Jacobs	2	2	7	0	0.10
3421	Eleanora Bersani	2	2	7	0	0.10
3422	Elizabeth Garcia	2	2	7	0	0.10
3423	Fang Fu	2	2	7	0	0.10
3424	Fang Hu	2	2	7	0	0.10
3425	Fang Luo	2	2	7	0	0.10
3426	Fang Yu	2	2	7	0	0.10
3427	Hector Davis	2	2	7	0	0.10
3428	Jie Meng	2	2	7	0	0.10
3429	Jing Xiao	2	2	7	0	0.10
3430	Juan Wan	2	2	7	0	0.10
3431	Kevin Burch	2	2	7	0	0.10
3432	Kimberly Moore	2	2	7	0	0.10
3433	Li Qian	2	2	7	0	0.10
3434	Li Tao	2	2	7	0	0.10
3435	Roger Orozco	2	2	7	0	0.10
3436	Tao Tao	2	2	7	0	0.10
3437	Timothy Moore	2	2	7	0	0.10
3438	William Brown	2	2	7	0	0.10
3439	Xia Wei	2	2	7	0	0.10
3440	Yong Zhou	2	2	7	0	0.10
3441	Mark Garcia	2	1	13	3	0.10
3442	Shannon Smith	2	1	13	3	0.10
3443	Adriano Tomei	2	2	3	1	0.10
3444	Aniela Wszoła	2	2	3	1	0.10
3445	Becky Walters	2	2	3	1	0.10
3446	Bernard-André Rolland	2	2	3	1	0.10
3447	Brad Martinez	2	2	3	1	0.10
3448	Brandon Hoffman	2	2	3	1	0.10
3449	Christelle Grégoire	2	2	3	1	0.10
3450	Daniel Smith	2	2	3	1	0.10
3451	Devansh Divan	2	2	3	1	0.10
3452	Elwira Heuser	2	2	3	1	0.10
3453	Fateh Dube	2	2	3	1	0.10
3454	Fiamma Baracca	2	2	3	1	0.10
3455	Gary Tucker	2	2	3	1	0.10
3456	Holly Love	2	2	3	1	0.10
3457	Isabelle Baudry de Michel	2	2	3	1	0.10
3458	Jacob Yang	2	2	3	1	0.10
3459	Jamie Schmidt	2	2	3	1	0.10
3460	Janice Schultz	2	2	3	1	0.10
3461	Jonathan Lewis	2	2	3	1	0.10
3462	Joseph Chen	2	2	3	1	0.10
3463	Joseph Ramirez	2	2	3	1	0.10
3464	Kenneth Randolph	2	2	3	1	0.10
3465	Larry Walters	2	2	3	1	0.10
3466	Laura Stanton	2	2	3	1	0.10
3467	Lodovico Interminei	2	2	3	1	0.10
3468	Macaria de Landa	2	2	3	1	0.10
3469	Mateusz Mikoda	2	2	3	1	0.10
3470	Matthew Carter	2	2	3	1	0.10
3471	Megan Morrow	2	2	3	1	0.10
3472	Misha Batta	2	2	3	1	0.10
3473	Robert Lynn	2	2	3	1	0.10
3474	Serafina Céspedes Sales	2	2	3	1	0.10
3475	Sherry Hernandez	2	2	3	1	0.10
3476	Sumer Kakar	2	2	3	1	0.10
3477	Timothy Riley	2	2	3	1	0.10
3478	Tobiasz Haponiuk	2	2	3	1	0.10
3479	Phillip Brooks	2	1	9	4	0.10
3480	Anna Glass	1	1	25	4	0.10
3481	Jamie Terry	1	1	25	4	0.10
3482	Kimberly Alvarez	1	1	25	4	0.10
3483	Nancy Macias	1	1	25	4	0.10
3484	Rebecca Robinson	1	1	25	4	0.10
3485	Samuel Thompson	1	1	25	4	0.10
3486	Nikolaos Jäkel	1	1	21	5	0.09
3487	Biju Swamy	2	2	6	0	0.09
3488	Chao Liu	2	2	6	0	0.09
3489	Chao Sun	2	2	6	0	0.09
3490	Dennis Mcdowell	2	2	6	0	0.09
3491	Elakshi Venkataraman	2	2	6	0	0.09
3492	Electric Collective	2	2	6	0	0.09
3493	Enzio Garibaldi	2	2	6	0	0.09
3494	Ethan Clarke	2	2	6	0	0.09
3495	Fang Mo	2	2	6	0	0.09
3496	Gabrielle Richardson	2	2	6	0	0.09
3497	Gang Deng	2	2	6	0	0.09
3498	Heidi Allen	2	2	6	0	0.09
3499	Jason Warner	2	2	6	0	0.09
3500	Jeffrey Watson	2	2	6	0	0.09
3501	Jing Dai	2	2	6	0	0.09
3502	Jing Dong	2	2	6	0	0.09
3503	Jing Yao	2	2	6	0	0.09
3504	Jonathan Marquez	2	2	6	0	0.09
3505	Juan Hall	2	2	6	0	0.09
3506	Jun Zeng	2	2	6	0	0.09
3507	Kara Lee	2	2	6	0	0.09
3508	Keya Kota	2	2	6	0	0.09
3509	Li Pan	2	2	6	0	0.09
3510	Lori Henderson	2	2	6	0	0.09
3511	Margaret Mccarthy	2	2	6	0	0.09
3512	Min Gu	2	2	6	0	0.09
3513	Min Tian	2	2	6	0	0.09
3514	Miss Allison King	2	2	6	0	0.09
3515	Ping Lu	2	2	6	0	0.09
3516	Qiang Tao	2	2	6	0	0.09
3517	Qiang Xia	2	2	6	0	0.09
3518	Ricciotti Traetta	2	2	6	0	0.09
3519	Shaan Jaggi	2	2	6	0	0.09
3520	Sophie Bennett	2	2	6	0	0.09
3521	Sosimo Donaire Marí	2	2	6	0	0.09
3522	Syncopated Symphony Collective	2	2	6	0	0.09
3523	Velvet Horizon	2	2	6	0	0.09
3524	Wei Zou	2	2	6	0	0.09
3525	Xiulan Qiao	2	2	6	0	0.09
3526	Xiulan Yu	2	2	6	0	0.09
3527	Xiuying Xue	2	2	6	0	0.09
3528	Yan Ma	2	2	6	0	0.09
3529	Yong Feng	2	2	6	0	0.09
3530	Minoru Nakajima	2	1	12	3	0.09
3531	Alexander Jenkins	2	2	2	1	0.09
3532	Amy Moore	2	2	2	1	0.09
3533	Angela Wallace	2	2	2	1	0.09
3534	Crossroads & Cipher	2	2	2	1	0.09
3535	Daniel Cooper	2	2	2	1	0.09
3536	Elizabeth Willis	2	2	2	1	0.09
3537	Emily Bush	2	2	2	1	0.09
3538	Eric Briggs	2	2	2	1	0.09
3539	Francisco Manning	2	2	2	1	0.09
3540	Frau Melissa Neuschäfer	2	2	2	1	0.09
3541	Gerold Mangold	2	2	2	1	0.09
3542	Jason Singleton	2	2	2	1	0.09
3543	Kimberly Rodriguez	2	2	2	1	0.09
3544	Mandy Murphy	2	2	2	1	0.09
3545	Margrit Stiffel	2	2	2	1	0.09
3546	Mary Smith	2	2	2	1	0.09
3547	Melissa Thomas	2	2	2	1	0.09
3548	Melissa Wilson	2	2	2	1	0.09
3549	Melissa Wyatt	2	2	2	1	0.09
3550	Monika Przewłoka	2	2	2	1	0.09
3551	Nikolas Naser	2	2	2	1	0.09
3552	Norbert Flisek	2	2	2	1	0.09
3553	Renato Holsten	2	2	2	1	0.09
3554	Seth Payne	2	2	2	1	0.09
3555	Stacy Hendricks	2	2	2	1	0.09
3556	Stilla Koch	2	2	2	1	0.09
3557	The Garage Dolls	2	2	2	1	0.09
3558	Walentina Ernst-Klingelhöfer	2	2	2	1	0.09
3559	Gwendolyn Fitzgerald	2	1	8	4	0.09
3560	Sharon Sanchez	2	1	19	1	0.09
3561	Xiulan Jin	2	1	19	1	0.09
3562	Christopher Gonzalez	1	1	24	4	0.09
3563	Emily Johnson	1	1	24	4	0.09
3564	Gary Torres	1	1	24	4	0.09
3565	Gregory Porter	1	1	24	4	0.09
3566	James Cain	1	1	24	4	0.09
3567	Leonard Dudley	1	1	24	4	0.09
3568	Lisa Page	1	1	24	4	0.09
3569	Patricia Johnson	1	1	24	4	0.09
3570	Thomas Mcconnell	1	1	24	4	0.09
3571	Xiuying Wen	1	1	24	4	0.09
3572	Pamela Johnson	2	1	15	2	0.09
3573	Jason Aguirre	1	1	20	5	0.09
3574	John Graves	1	1	20	5	0.09
3575	Matthew Lane	1	1	20	5	0.09
3576	Sheryl Roman	1	1	20	5	0.09
3577	Joseph Luna	3	1	6	0	0.09
3578	Sylvia Watson	3	1	6	0	0.09
3579	Aiden Harper	2	2	5	0	0.09
3580	Antonina Zaccardo	2	2	5	0	0.09
3581	Balkan Prisms	2	2	5	0	0.09
3582	Beth Gray	2	2	5	0	0.09
3583	Brandi Riley	2	2	5	0	0.09
3584	Brita Holsten	2	2	5	0	0.09
3585	Chao Zou	2	2	5	0	0.09
3586	Fang Wen	2	2	5	0	0.09
3587	Finn Morgan	2	2	5	0	0.09
3588	Frank Johnson	2	2	5	0	0.09
3589	Heinz-Gerd Klapp	2	2	5	0	0.09
3590	Highway Vibes	2	2	5	0	0.09
3591	Jessica Lewis	2	2	5	0	0.09
3592	Jing Xia	2	2	5	0	0.09
3593	Jing Zhao	2	2	5	0	0.09
3594	Johnathan Sanchez	2	2	5	0	0.09
3595	Kinship Harmony	2	2	5	0	0.09
3596	Lei Su	2	2	5	0	0.09
3597	Lidia Battisti-Righi	2	2	5	0	0.09
3598	Marianela Vila Zaragoza	2	2	5	0	0.09
3599	Megan Baird	2	2	5	0	0.09
3600	Michael Peterson	2	2	5	0	0.09
3601	Rita Finzi	2	2	5	0	0.09
3602	Romana Iadanza	2	2	5	0	0.09
3603	Skylar Brooks	2	2	5	0	0.09
3604	Steven Boyd	2	2	5	0	0.09
3605	Tao Luo	2	2	5	0	0.09
3606	Xavier Gaillard	2	2	5	0	0.09
3607	Xiulan Wan	2	2	5	0	0.09
3608	Alicia Robertson	1	1	16	6	0.09
3609	Amanda Ochoa	2	2	1	1	0.09
3610	Blake Rodriguez	2	2	1	1	0.09
3611	Bryan Sanchez	2	2	1	1	0.09
3612	Carrie Martin	2	2	1	1	0.09
3613	Charles Wallace	2	2	1	1	0.09
3614	Concetta Klotz	2	2	1	1	0.09
3615	Douglas Lewis	2	2	1	1	0.09
3616	Elizabeth Cochran PhD	2	2	1	1	0.09
3617	Evelien Becht-Versluijs	2	2	1	1	0.09
3618	Nanami Watanabe	2	2	1	1	0.09
3619	Navya Rana	2	2	1	1	0.09
3620	Ronald Brooks	2	2	1	1	0.09
3621	Sophia Dippel	2	2	1	1	0.09
3622	Susan Willis	2	2	1	1	0.09
3623	Whitney Taylor	2	2	1	1	0.09
3624	Yoichi Sato	2	2	1	1	0.09
3625	Yvonne Alexander	2	2	1	1	0.09
3626	Zeeshan Dora	2	2	1	1	0.09
3627	Éric Daniel	2	2	1	1	0.09
3628	Adrien Weiss	1	1	23	4	0.09
3629	Charles du Perez	1	1	23	4	0.09
3630	Damon Stewart	1	1	23	4	0.09
3631	Pauline Guyot de la Maillet	1	1	23	4	0.09
3632	Ryohei Sato	1	1	23	4	0.09
3633	Steve Washington	1	1	23	4	0.09
3634	James Thomas	1	1	19	5	0.09
3635	Thomas Bradley	1	1	19	5	0.09
3636	Xia Kong	1	1	19	5	0.09
3637	Baudelio Anglada-Valenzuela	2	2	4	0	0.09
3638	Charles Delaunay	2	2	4	0	0.09
3639	Elizabeth Mccarthy	2	2	4	0	0.09
3640	Hans-Josef Killer	2	2	4	0	0.09
3641	James Williams	2	2	4	0	0.09
3642	Kerri Weeks	2	2	4	0	0.09
3643	Lauren Foster	2	2	4	0	0.09
3644	Lauren Powell	2	2	4	0	0.09
3645	Louis Armstrong	2	2	4	0	0.09
3646	Marc Raymond	2	2	4	0	0.09
3647	Miloš Pokorný	2	2	4	0	0.09
3648	Mirja Jacob	2	2	4	0	0.09
3649	Mohamed Sastre Godoy	2	2	4	0	0.09
3650	N.V. Blake	2	2	4	0	0.09
3651	Prudencio Nilo Arjona Huguet	2	2	4	0	0.09
3652	Sahil Khalsa	2	2	4	0	0.09
3653	Wei Wei	2	2	4	0	0.09
3654	Knarren	2	1	10	3	0.09
3655	Angela Thompson	2	1	17	1	0.09
3656	Cynthia Nunez	2	1	17	1	0.09
3657	Veronica Ferguson	2	1	17	1	0.09
3658	Friedl Bauer-Hövel	1	1	22	4	0.09
3659	Hartmut Ruppert	1	1	22	4	0.09
3660	Heiderose Putz	1	1	22	4	0.09
3661	Herr Hans Dieter Hentschel	1	1	22	4	0.09
3662	Hertha Schinke	1	1	22	4	0.09
3663	Shray Agarwal	1	1	7	8	0.09
3664	Kimberly Estrada	1	1	18	5	0.09
3665	Édouard-Rémy Clerc	1	1	18	5	0.09
3666	Lucas Bennett	3	1	4	0	0.09
3667	Maya Torres	3	1	4	0	0.09
3668	Olivia Carter	3	1	4	0	0.09
3669	Ashley Brooks	2	2	3	0	0.09
3670	Bethany Jennings	2	2	3	0	0.09
3671	Brittany Harvey	2	2	3	0	0.09
3672	Carlos Duffy	2	2	3	0	0.09
3673	Colin Wood	2	2	3	0	0.09
3674	Elizabeth Parker	2	2	3	0	0.09
3675	Herr Folkert Vogt	2	2	3	0	0.09
3676	Ivana Vávrová	2	2	3	0	0.09
3677	Jie Chen	2	2	3	0	0.09
3678	Jun Xue	2	2	3	0	0.09
3679	Justin Berry	2	2	3	0	0.09
3680	Kathryn Mcknight	2	2	3	0	0.09
3681	Lila Rivers	2	2	3	0	0.09
3682	Mia Waters	2	2	3	0	0.09
3683	Nancy Arnold	2	2	3	0	0.09
3684	Ritvik Raman	2	2	3	0	0.09
3685	William Knapp	2	2	3	0	0.09
3686	Dawid Golda	1	1	14	6	0.09
3687	Brandi Noble	1	1	25	3	0.09
3688	Hailey Hak	1	1	25	3	0.09
3689	Lynn Brumleve-van der Horst	1	1	25	3	0.09
3690	Maud van de Pol	1	1	25	3	0.09
3691	Mohamed van de Berg	1	1	25	3	0.09
3692	Olaf Otto	1	1	25	3	0.09
3693	Otmar Fröhlich	1	1	25	3	0.09
3694	Richard Sweeney	1	1	25	3	0.09
3695	Cody Moore	2	1	16	1	0.09
3696	Jacqueline Young	2	1	16	1	0.09
3697	John Ray	2	1	16	1	0.09
3698	Kyle Hill	2	1	16	1	0.09
3699	Kevin Davidson	1	1	21	4	0.09
3700	Nicole Clark	1	1	21	4	0.09
3701	Alicia Heinz	2	1	12	2	0.09
3702	Ilona Stolze	2	1	12	2	0.09
3703	Rose-Marie Schmidtke	2	1	12	2	0.09
3704	Xiuying Wei	2	1	12	2	0.09
3705	Francisco Lyons	1	1	17	5	0.09
3706	Hector Porcel Gomis	1	1	17	5	0.09
3707	Nicolasa Zaragoza Asenjo	1	1	17	5	0.09
3708	Clara Henderson	2	2	2	0	0.09
3709	Daniel Aubert-Brunet	2	2	2	0	0.09
3710	Ethan Crowley	2	2	2	0	0.09
3711	Friedrich-Wilhelm Ullmann	2	2	2	0	0.09
3712	Inès-Adèle Valette	2	2	2	0	0.09
3713	Jing Wan	2	2	2	0	0.09
3714	Mark Beck	2	2	2	0	0.09
3715	Mary Brooks	2	2	2	0	0.09
3716	Melissa Harris	2	2	2	0	0.09
3717	Milo Knight	2	2	2	0	0.09
3718	Odette-Inès Lelièvre	2	2	2	0	0.09
3719	Renee Lynch	2	2	2	0	0.09
3720	Zara Quinn	2	2	2	0	0.09
3721	Jie Gao	1	1	24	3	0.09
3722	Kara Frazier	1	1	24	3	0.09
3723	Andrew Jenkins	2	1	15	1	0.09
3724	Matthew Salazar	2	1	15	1	0.09
3725	Antonietta Zamengo-Zanazzo	1	1	20	4	0.09
3726	Douglas Gonzalez	1	1	20	4	0.09
3727	Joseph Beltran	1	1	20	4	0.09
3728	Patrizio Antonello-Salvemini	1	1	20	4	0.09
3729	Troy Ruiz	1	1	20	4	0.09
3730	Charles Mccoy	1	1	16	5	0.09
3731	Suhana Din	1	1	16	5	0.09
3732	Anya Krishnan	2	2	1	0	0.09
3733	Denise Knight	2	2	1	0	0.09
3734	Derek Cline	2	2	1	0	0.09
3735	Edward Henry	2	2	1	0	0.09
3736	Ishita Bahri	2	2	1	0	0.09
3737	Krish Wali	2	2	1	0	0.09
3738	Melissa Warren	2	2	1	0	0.09
3739	Nirvi Chahal	2	2	1	0	0.09
3740	Pihu Gulati	2	2	1	0	0.09
3741	Siya Sahni	2	2	1	0	0.09
3742	Alan Wilson	1	1	23	3	0.09
3743	Bradley Wright	1	1	23	3	0.09
3744	Douglas Turner	1	1	23	3	0.09
3745	Gregory Wright	1	1	23	3	0.09
3746	Stephen Ramirez	1	1	23	3	0.09
3747	Cheryl Suarez	2	1	14	1	0.09
3748	Jacek Gorol	1	1	8	7	0.09
3749	Tadeusz Majorek	1	1	8	7	0.09
3750	Geneviève Duhamel	1	1	19	4	0.09
3751	Johan Hermann	1	1	19	4	0.09
3752	Katie Hartman	1	1	19	4	0.09
3753	Magnus Geisler	1	1	19	4	0.09
3754	Matthew Good	1	1	19	4	0.09
3755	Michel Dippel	1	1	19	4	0.09
3756	Brigitte Gotthard	2	1	10	2	0.08
3757	Yong Yang	2	1	10	2	0.08
3758	Bryan Davis	1	1	15	5	0.08
3759	Gina Cox	1	1	15	5	0.08
3760	Sherry Martin	1	1	15	5	0.08
3761	Rigo Becker	2	2	0	0	0.08
3762	Curtis Underwood	1	1	22	3	0.08
3763	Franklin Allen	1	1	22	3	0.08
3764	Jolanta Zirme	1	1	22	3	0.08
3765	Siegmund Winkler	1	1	22	3	0.08
3766	Wendy Porter	1	1	22	3	0.08
3767	Brandon Wilson	2	1	13	1	0.08
3768	Jeanne Miller	2	1	13	1	0.08
3769	Wei Zhong	2	1	13	1	0.08
3770	Sandra Majak	1	1	7	7	0.08
3771	Alexandra Obrien	1	1	18	4	0.08
3772	Carmine Harloff	1	1	18	4	0.08
3773	Cynthia Reed	1	1	18	4	0.08
3774	Ida Stolze-Lübs	1	1	18	4	0.08
3775	Juan Zhao	1	1	18	4	0.08
3776	Kerry Henry	1	1	18	4	0.08
3777	Marcus Berry	1	1	18	4	0.08
3778	Michele Sharp	1	1	18	4	0.08
3779	Gang Xiong	2	1	9	2	0.08
3780	Karina Gunpf	2	1	9	2	0.08
3781	Jun Hou	1	1	14	5	0.08
3782	Steven Ray	1	1	14	5	0.08
3783	Jessica Lindsey	1	1	25	2	0.08
3784	Chao Su	1	1	21	3	0.08
3785	Christopher Leonard	1	1	21	3	0.08
3786	Ethan Sanchez	1	1	21	3	0.08
3787	Tao Hao	1	1	21	3	0.08
3788	Jun Lai	2	1	12	1	0.08
3789	Arthur Sparks	1	1	17	4	0.08
3790	Betti Löffler	1	1	17	4	0.08
3791	Florence Martin	1	1	17	4	0.08
3792	Ida Loos	1	1	17	4	0.08
3793	Krystal Martin	1	1	17	4	0.08
3794	Laura Hernandez	1	0	23	7	0.08
3795	Zachary Stanley	1	0	23	7	0.08
3796	Collective Conscience	2	1	8	2	0.08
3797	Melissa Smith	2	1	15	0	0.08
3798	Anna Gonzales	1	1	20	3	0.08
3799	Gerhild Schweitzer	1	1	20	3	0.08
3800	Hasso Kreusel	1	1	20	3	0.08
3801	Jasmine Moore	1	1	20	3	0.08
3802	Joy Wood	1	1	20	3	0.08
3803	Mathilde Hesse	1	1	20	3	0.08
3804	Qiang Liao	1	1	20	3	0.08
3805	Tina Watson	1	1	20	3	0.08
3806	Yan Zheng	1	1	20	3	0.08
3807	Bas Jansen	1	1	16	4	0.08
3808	Hailey Timmerman-van der Berg	1	1	16	4	0.08
3809	Lily Sprong	1	1	16	4	0.08
3810	Sherri Mclaughlin	1	1	12	5	0.08
3811	Kendra Walsh	1	1	23	2	0.08
3812	Kyle Reynolds	1	1	23	2	0.08
3813	Nicholas Patel	1	1	23	2	0.08
3814	Alan Mcgee	1	1	19	3	0.08
3815	Amanda Hunter	1	1	19	3	0.08
3816	Antonio Fuentes	1	1	19	3	0.08
3817	Arnaude Poirier	1	1	19	3	0.08
3818	Brenda Anderson	1	1	19	3	0.08
3819	Cheryl Martinez	1	1	19	3	0.08
3820	Christophe-Émile Grégoire	1	1	19	3	0.08
3821	Georges Chrétien	1	1	19	3	0.08
3822	Jaime Watkins	1	1	19	3	0.08
3823	Jonathan Avila	1	1	19	3	0.08
3824	Lisa Shaw	1	1	19	3	0.08
3825	Lori Williams	1	1	19	3	0.08
3826	Luce Loiseau	1	1	19	3	0.08
3827	Mark Barr	1	1	19	3	0.08
3828	Mark Thomas	1	1	19	3	0.08
3829	Min Gong	1	1	19	3	0.08
3830	Nicolas Gosselin du Robert	1	1	19	3	0.08
3831	Tracy Bennett	1	1	19	3	0.08
3832	William Carter	1	1	19	3	0.08
3833	Jing Huang	2	1	10	1	0.08
3834	Yan Sun	2	1	10	1	0.08
3835	Alan Clark	1	1	15	4	0.08
3836	Andrew Martin	1	1	15	4	0.08
3837	Emily Drake	1	1	15	4	0.08
3838	Tamara Miller	1	1	15	4	0.08
3839	Greca Matteotti-Jovinelli	1	1	11	5	0.08
3840	Michelotto Toldo	1	1	11	5	0.08
3841	April Anderson	1	1	22	2	0.08
3842	Dana Walters	1	1	22	2	0.08
3843	Daniel Edwards	1	1	22	2	0.08
3844	Deborah Brown	1	1	22	2	0.08
3845	Derek Cook	1	1	22	2	0.08
3846	Elizabeth West	1	1	22	2	0.08
3847	Jared Arnold	1	1	22	2	0.08
3848	Jeremy Macias	1	1	22	2	0.08
3849	Jonathan Garcia	1	1	22	2	0.08
3850	Ralph Johnston	1	1	22	2	0.08
3851	Stacy Bryant	1	1	22	2	0.08
3852	Tanya Evans	1	1	22	2	0.08
3853	Todd Jones	1	1	22	2	0.08
3854	Victoria Butler	1	1	22	2	0.08
3855	Darlene Watson	1	1	18	3	0.08
3856	Darryl Curtis	1	1	18	3	0.08
3857	Janette Schleich	1	1	18	3	0.08
3858	Joseph Reyes	1	1	18	3	0.08
3859	Kevin Pruitt	1	1	18	3	0.08
3860	Marcel Kreusel	1	1	18	3	0.08
3861	Maria Powell	1	1	18	3	0.08
3862	Mikayla Russell	1	1	18	3	0.08
3863	Ralf Budig	1	1	18	3	0.08
3864	Carrie Cobb	1	1	14	4	0.08
3865	Gilles Raymond	1	1	14	4	0.08
3866	Justin Sullivan	1	1	14	4	0.08
3867	Kathleen Carroll	1	1	14	4	0.08
3868	Kenneth Knight	1	1	14	4	0.08
3869	Tammy Higgins	1	1	14	4	0.08
3870	Tiffany Lutz	1	1	14	4	0.08
3871	Nathaniel Craig	2	1	5	2	0.08
3872	Denise Barthelemy de Brunet	1	1	21	2	0.08
3873	Dina Scholten	1	1	21	2	0.08
3874	Jamie Garcia	1	1	21	2	0.08
3875	Kornelia Michnik	1	1	21	2	0.08
3876	Kristopher Roberts	1	1	21	2	0.08
3877	Na Qiao	1	1	21	2	0.08
3878	Nathalie Beer-Huhn	1	1	21	2	0.08
3879	Nicola Strangio	1	1	21	2	0.08
3880	Nicole Murphy	1	1	21	2	0.08
3881	Roberto Govoni	1	1	21	2	0.08
3882	Sherry Salinas	1	1	21	2	0.08
3883	Stephanie Murray	1	1	21	2	0.08
3884	Annunziata Trezzini	1	1	17	3	0.08
3885	Donna Vecellio	1	1	17	3	0.08
3886	Fulvio Comisso	1	1	17	3	0.08
3887	Jim Miller	1	1	17	3	0.08
3888	Monica Thompson	1	1	17	3	0.08
3889	Ping Xiao	1	1	17	3	0.08
3890	Sophia Cilea	1	1	17	3	0.08
3891	Steven Goodman	1	1	17	3	0.08
3892	Alexandra Taylor	1	1	13	4	0.08
3893	Baudelio Naranjo Ríos	1	1	13	4	0.08
3894	Birger Gutknecht	1	1	13	4	0.08
3895	Cameron Cohen	1	1	13	4	0.08
3896	Charvi Bains	1	1	13	4	0.08
3897	Haley Campbell	1	1	13	4	0.08
3898	Heinz-Günter Schmiedecke	1	1	13	4	0.08
3899	Holm Segebahn	1	1	13	4	0.08
3900	Hrishita Sura	1	1	13	4	0.08
3901	Ibrahim Heerkens-Everde	1	1	13	4	0.08
3902	Klaus-Werner Gerlach	1	1	13	4	0.08
3903	Megan Bowers	1	1	13	4	0.08
3904	Paca Ema Mosquera Cepeda	1	1	13	4	0.08
3905	Plinio Ribas	1	1	13	4	0.08
3906	Robby Schmiedecke	1	1	13	4	0.08
3907	Rocío Rovira	1	1	13	4	0.08
3908	Ulises Gomila Morell	1	1	13	4	0.08
3909	Valerie Levine	1	1	13	4	0.08
3910	Xiuying Yang	1	1	13	4	0.08
3911	Astrid Jockel-Hecker	1	1	24	1	0.07
3912	Denis Pechel	1	1	24	1	0.07
3913	Eckehard Kabus-Barth	1	1	24	1	0.07
3914	Elias Beyer	1	1	24	1	0.07
3915	Erhard Blümel	1	1	24	1	0.07
3916	Ernst-August Hamann-Junck	1	1	24	1	0.07
3917	Franco Dietz	1	1	24	1	0.07
3918	Gerfried Schaaf	1	1	24	1	0.07
3919	Inna Wagner	1	1	24	1	0.07
3920	Irmtrud Jessel	1	1	24	1	0.07
3921	Isabelle Bloch	1	1	24	1	0.07
3922	Ivonne Austermühle	1	1	24	1	0.07
3923	Juan Brown	1	1	24	1	0.07
3924	Margarete Löchel	1	1	24	1	0.07
3925	Mirja Heintze	1	1	24	1	0.07
3926	Pawel Zirme	1	1	24	1	0.07
3927	Sarah Miles	1	1	24	1	0.07
3928	Sevim Holt	1	1	24	1	0.07
3929	Sieghard Jäntsch-Kensy	1	1	24	1	0.07
3930	Jo Baker	1	1	9	5	0.07
3931	Brandon Griffith	1	1	20	2	0.07
3932	Dominik Preiß	1	1	20	2	0.07
3933	Ping Liu	1	1	20	2	0.07
3934	Jun Liao	2	1	11	0	0.07
3935	Adira Chaudhuri	1	1	5	6	0.07
3936	Bart Wensen-Kof	1	1	16	3	0.07
3937	Danny Fitzgerald	1	1	16	3	0.07
3938	Eric Hopkins	1	1	16	3	0.07
3939	Kristina Sanchez	1	1	16	3	0.07
3940	Lieve van Riet	1	1	16	3	0.07
3941	Lizz van Santen	1	1	16	3	0.07
3942	Mary Jacobs	1	1	16	3	0.07
3943	Melissa Rush	1	1	16	3	0.07
3944	Min Yang	1	1	16	3	0.07
3945	Nina Mallien	1	1	16	3	0.07
3946	Ryan Mcguire	1	1	16	3	0.07
3947	Stephen Allen	1	1	16	3	0.07
3948	Tanya Ratta	1	1	16	3	0.07
3949	Ulf Kusch	2	1	7	1	0.07
3950	Gitta Wilmsen	1	1	12	4	0.07
3951	Larry Mendez	1	1	12	4	0.07
3952	Lisa Crawford	2	1	3	2	0.07
3953	Kimberly Dominguez	1	1	8	5	0.07
3954	Ricky Franklin	1	1	8	5	0.07
3955	Alexander Carr	1	1	19	2	0.07
3956	Amanda Peterson	1	1	19	2	0.07
3957	Baptist Gierschner	1	1	19	2	0.07
3958	Colleen Richards	1	1	19	2	0.07
3959	Elizabeth Bradshaw	1	1	19	2	0.07
3960	Joseph Ho	1	1	19	2	0.07
3961	Madeleine Wulf	1	1	19	2	0.07
3962	Rosalie Hörle	1	1	19	2	0.07
3963	Savannah Shaw	1	1	19	2	0.07
3964	Anthony Smith	2	1	10	0	0.07
3965	Guiying Qian	2	1	10	0	0.07
3966	Qiang Duan	2	1	10	0	0.07
3967	Jing Xu	1	1	15	3	0.07
3968	Kai-Uwe Döring	1	1	15	3	0.07
3969	Robert Allen	1	1	15	3	0.07
3970	Yoko Murakami	1	1	15	3	0.07
3971	Annunziata Bertoli-Ruberto	2	1	6	1	0.07
3972	Antoine du Adam	2	1	6	1	0.07
3973	Augustin Raymond	2	1	6	1	0.07
3974	Chantal Gérard	2	1	6	1	0.07
3975	Claudine Leleu	2	1	6	1	0.07
3976	Julien Collin-Texier	2	1	6	1	0.07
3977	Min Han	2	1	6	1	0.07
3978	Sylvie-Chantal Schmitt	2	1	6	1	0.07
3979	Eva Sani	1	1	11	4	0.07
3980	Lia Wähner-Weimer	1	1	11	4	0.07
3981	Stacy Hudson	1	1	11	4	0.07
3982	Steven Carter	1	1	11	4	0.07
3983	Feliciano Noguera-Vallejo	2	1	2	2	0.07
3984	Natasza Sygut	1	1	7	5	0.07
3985	Pamela Graham	1	1	7	5	0.07
3986	Aaron Conway	1	1	18	2	0.07
3987	Adam Hanna	1	1	18	2	0.07
3988	Alexandria Spencer	1	1	18	2	0.07
3989	Allison Johnson	1	1	18	2	0.07
3990	Amber Franklin	1	1	18	2	0.07
3991	Chris Meyer	1	1	18	2	0.07
3992	Fang Shao	1	1	18	2	0.07
3993	Jody Lopez	1	1	18	2	0.07
3994	Leslie Wagner	1	1	18	2	0.07
3995	Marie Flores	1	1	18	2	0.07
3996	Ming Peng	1	1	18	2	0.07
3997	Molly Williams	1	1	18	2	0.07
3998	Sabrina Mcmahon	1	1	18	2	0.07
3999	William Stone	1	1	18	2	0.07
4000	George Jones	2	1	9	0	0.07
4001	Ming Deng	2	1	9	0	0.07
4002	Nicole Smith	2	1	9	0	0.07
4003	Anaya Randhawa	1	1	14	3	0.07
4004	Anvi Chawla	1	1	14	3	0.07
4005	Steven Green	1	1	14	3	0.07
4006	Yuvaan Tripathi	1	1	14	3	0.07
4007	Carla Gross	2	1	5	1	0.07
4008	Craig Aguilar	1	1	10	4	0.07
4009	Jon Peters	1	1	10	4	0.07
4010	Jeffery Williams	1	1	21	1	0.07
4011	Šimon Urban	1	1	21	1	0.07
4012	Devansh Seshadri	1	1	6	5	0.07
4013	Alexander Whitney	1	1	17	2	0.07
4014	Angela Porter	1	1	17	2	0.07
4015	Edwin Binner	1	1	17	2	0.07
4016	Evelyn Gordon	1	1	17	2	0.07
4017	Gang Wang	1	1	17	2	0.07
4018	Gernot Karz	1	1	17	2	0.07
4019	Gregory White	1	1	17	2	0.07
4020	Hansjoachim Sager	1	1	17	2	0.07
4021	Ian Jüttner	1	1	17	2	0.07
4022	James Maldonado	1	1	17	2	0.07
4023	Joseph Parsons	1	1	17	2	0.07
4024	Joshua Anderson	1	1	17	2	0.07
4025	Lori Gregory	1	1	17	2	0.07
4026	Mary Cisneros	1	1	17	2	0.07
4027	Matthew Fields	1	1	17	2	0.07
4028	Richard Frazier	1	1	17	2	0.07
4029	Rodney Clark	1	1	17	2	0.07
4030	Sonia Weeks	1	1	17	2	0.07
4031	Vanessa Clark	1	1	17	2	0.07
4032	Yang Shao	2	1	8	0	0.07
4033	Anaïs Peron	1	1	13	3	0.07
4034	Caleb Williamson	1	1	13	3	0.07
4035	Chad Green	1	1	13	3	0.07
4036	Diana Lledó Colomer	1	1	13	3	0.07
4037	Donald Bradley	1	1	13	3	0.07
4038	Jaime Arrieta	1	1	13	3	0.07
4039	Justin Wyatt	1	1	13	3	0.07
4040	Katie Johnston	1	1	13	3	0.07
4041	Livio Brenna	1	1	13	3	0.07
4042	Mai Yamada	1	1	13	3	0.07
4043	Margaret Woods	1	1	13	3	0.07
4044	Nino Pasolini	1	1	13	3	0.07
4045	Claire Garner	2	1	4	1	0.07
4046	Lauren Thomas	2	1	4	1	0.07
4047	Sheryl Massey	2	1	4	1	0.07
4048	Jacob Hunter	1	1	9	4	0.07
4049	Arkadiusz Ciesielka	1	1	5	5	0.07
4050	Adrien Boulay	1	1	16	2	0.07
4051	Alphonse-Jacques Ledoux	1	1	16	2	0.07
4052	Andre Scott	1	1	16	2	0.07
4053	Anna Mendez	1	1	16	2	0.07
4054	Annette Mckee	1	1	16	2	0.07
4055	April Morris	1	1	16	2	0.07
4056	Arthur Ollivier	1	1	16	2	0.07
4057	Athanasios Hamann	1	1	16	2	0.07
4058	Brianna Wilson	1	1	16	2	0.07
4059	Christopher Harris	1	1	16	2	0.07
4060	Eric Bartlett	1	1	16	2	0.07
4061	Gabriel Ferreira-Gillet	1	1	16	2	0.07
4062	James Harrington	1	1	16	2	0.07
4063	John Barnes	1	1	16	2	0.07
4064	Kelly Cummings	1	1	16	2	0.07
4065	Leah Farrell	1	1	16	2	0.07
4066	Lori Bass	1	1	16	2	0.07
4067	Lucie Prévost du Rousset	1	1	16	2	0.07
4068	Michael Carroll	1	1	16	2	0.07
4069	Michelle Garrett	1	1	16	2	0.07
4070	Milo Badoer	1	1	16	2	0.07
4071	Misty Reed	1	1	16	2	0.07
4072	Mitchell Hernandez	1	1	16	2	0.07
4073	Monica Hale	1	1	16	2	0.07
4074	Robert Adams	1	1	16	2	0.07
4075	Roderich Mohaupt	1	1	16	2	0.07
4076	Sara Shannon	1	1	16	2	0.07
4077	Steve Yu	1	1	16	2	0.07
4078	Susan Oneal	1	1	16	2	0.07
4079	Teresa White	1	1	16	2	0.07
4080	Thomas Beard	1	1	16	2	0.07
4081	Tiffany Lee	1	1	16	2	0.07
4082	Tonya Gallegos	1	1	16	2	0.07
4083	Jie Hao	2	1	7	0	0.07
4084	Kenneth Jones	2	1	7	0	0.07
4085	Kanav Sachar	1	1	12	3	0.07
4086	Melinda Austin	1	1	12	3	0.07
4087	Nayantara Bhatti	1	1	12	3	0.07
4088	Shray Kala	1	1	12	3	0.07
4089	Chad Garcia	2	1	3	1	0.07
4090	Donato Mocenigo	2	1	3	1	0.07
4091	Michael Snow	2	0	20	1	0.07
4092	Klara Kuźnia	1	1	8	4	0.07
4093	Arthur Miller	1	1	19	1	0.07
4094	Joshua Thomas	1	1	19	1	0.07
4095	Kathleen Bean	1	1	19	1	0.07
4096	Luis King	1	1	19	1	0.07
4097	Peggy West	1	1	19	1	0.07
4098	Sydney Gonzalez	1	1	19	1	0.07
4099	Felix Hornich	2	0	16	2	0.07
4100	Alex Ramsey	1	1	15	2	0.07
4101	Angela Kelly	1	1	15	2	0.07
4102	Ashley Larson	1	1	15	2	0.07
4103	Ashley Murphy	1	1	15	2	0.07
4104	Beatrix Schmidt	1	1	15	2	0.07
4105	Beth Thompson	1	1	15	2	0.07
4106	Bjorn van Berkum-Freshour	1	1	15	2	0.07
4107	Brian Moore	1	1	15	2	0.07
4108	Carmina Esteve Coloma	1	1	15	2	0.07
4109	Christina Garrett	1	1	15	2	0.07
4110	Daan VI-Cadefau	1	1	15	2	0.07
4111	Fang Yang	1	1	15	2	0.07
4112	Gary Chapman	1	1	15	2	0.07
4113	Hans-Karl Hövel	1	1	15	2	0.07
4114	Isabelle Rijn-Höning	1	1	15	2	0.07
4115	Jake Marchal	1	1	15	2	0.07
4116	Jeffrey Tate	1	1	15	2	0.07
4117	John Gallagher	1	1	15	2	0.07
4118	John Price	1	1	15	2	0.07
4119	Joshua Crawford	1	1	15	2	0.07
4120	Kathleen Martinez	1	1	15	2	0.07
4121	Kenneth West	1	1	15	2	0.07
4122	Larry Thomas	1	1	15	2	0.07
4123	Laura Guerrero Cañete	1	1	15	2	0.07
4124	Laura Lindsey	1	1	15	2	0.07
4125	Marina Stiebitz	1	1	15	2	0.07
4126	Melinda Vasquez	1	1	15	2	0.07
4127	Michael Kelly	1	1	15	2	0.07
4128	Nancy Gonzalez	1	1	15	2	0.07
4129	Olaf Schmiedt	1	1	15	2	0.07
4130	Pim de Roo	1	1	15	2	0.07
4131	Priska Hölzenbecher	1	1	15	2	0.07
4132	Qiang Ma	1	1	15	2	0.07
4133	Rania Banik	1	1	15	2	0.07
4134	Riya Ahluwalia	1	1	15	2	0.07
4135	Robert Rodriguez	1	1	15	2	0.07
4136	Ryan Reynolds	1	1	15	2	0.07
4137	Sarah Lee	1	1	15	2	0.07
4138	Sarah Young	1	1	15	2	0.07
4139	Stephen Ross	1	1	15	2	0.07
4140	Valerie Phillips	1	1	15	2	0.07
4141	Wayne Perkins	1	1	15	2	0.07
4142	William Duncan	1	1	15	2	0.07
4143	Yasuhiro Sato	1	1	15	2	0.07
4144	Borderlands	2	1	6	0	0.07
4145	Yong Ye	2	1	6	0	0.07
4146	Armaan Bera	1	1	11	3	0.07
4147	Benjamin Griffin	1	1	11	3	0.07
4148	Henry Scott	1	1	11	3	0.07
4149	Herr Ilhan Mülichen	1	1	11	3	0.07
4150	Gustaw Reich	1	1	7	4	0.06
4151	Jerzy Bochnak	1	1	7	4	0.06
4152	Leon Misiurek	1	1	7	4	0.06
4153	Marianna Polasik	1	1	7	4	0.06
4154	John Schmitt	1	1	18	1	0.06
4155	Kevin Parker	1	1	18	1	0.06
4156	Adam Christian	1	1	14	2	0.06
4157	Caitlin Miller	1	1	14	2	0.06
4158	Catherine Brooks	1	1	14	2	0.06
4159	Christopher Robbins	1	1	14	2	0.06
4160	Gang Liang	1	1	14	2	0.06
4161	Herbert Mcfarland	1	1	14	2	0.06
4162	Joseph Ponce	1	1	14	2	0.06
4163	Kevin King	1	1	14	2	0.06
4164	Laura Mejia	1	1	14	2	0.06
4165	Matthew Lara	1	1	14	2	0.06
4166	Megan Harris	1	1	14	2	0.06
4167	Ming Liao	1	1	14	2	0.06
4168	Nicholas Harris	1	1	14	2	0.06
4169	Rachel Montes	1	1	14	2	0.06
4170	Rebecca Wise	1	1	14	2	0.06
4171	Rosalinde Tintzmann	1	1	14	2	0.06
4172	Sean James	1	1	14	2	0.06
4173	Sebastian Vega Saldaña	1	1	14	2	0.06
4174	Stacey Martin	1	1	14	2	0.06
4175	Xiuying Qiao	1	1	14	2	0.06
4176	Yong Qin	1	1	14	2	0.06
4177	Elizabeth Gonzalez	2	1	5	0	0.06
4178	Katelyn Flores	2	1	5	0	0.06
4179	Nicole Miller	2	1	5	0	0.06
4180	Aradhya Dara	1	1	10	3	0.06
4181	Armaan Dalal	1	1	10	3	0.06
4182	Brian Mclaughlin	1	1	10	3	0.06
4183	Devin Berger	1	1	10	3	0.06
4184	Drishya Srinivasan	1	1	10	3	0.06
4185	Garrett Montoya	1	1	10	3	0.06
4186	Henry Gross	1	1	10	3	0.06
4187	Joanna Avery	1	1	10	3	0.06
4188	John Rodriguez	1	1	10	3	0.06
4189	Judy Yang	1	1	10	3	0.06
4190	Marcus Stewart	1	1	10	3	0.06
4191	Miss Jennifer Williams	1	1	10	3	0.06
4192	Molly Gonzalez	1	1	10	3	0.06
4193	William Robinson	1	1	10	3	0.06
4194	Jonathan Guerrero	2	1	1	1	0.06
4195	Autumn Johnson	1	1	17	1	0.06
4196	Craig Campbell	1	1	17	1	0.06
4197	Lauren Rich	1	1	17	1	0.06
4198	Li Peng	1	1	17	1	0.06
4199	Ramona Mezzetta	1	1	17	1	0.06
4200	Sydney James	1	1	17	1	0.06
4201	Alexis Dixon	1	1	13	2	0.06
4202	Alicia Koch	1	1	13	2	0.06
4203	Alina Leblanc	1	1	13	2	0.06
4204	Ana Sager	1	1	13	2	0.06
4205	Andrea Meyer	1	1	13	2	0.06
4206	Ann Schmidt	1	1	13	2	0.06
4207	Anthony Scott	1	1	13	2	0.06
4208	Audrey Gray	1	1	13	2	0.06
4209	Austin Perez	1	1	13	2	0.06
4210	Chad Gonzales	1	1	13	2	0.06
4211	Chao Gu	1	1	13	2	0.06
4212	Christoph Hermann	1	1	13	2	0.06
4213	Christopher Hughes	1	1	13	2	0.06
4214	Constance Meyer	1	1	13	2	0.06
4215	David Nash	1	1	13	2	0.06
4216	Debra Curry	1	1	13	2	0.06
4217	Denise Poole	1	1	13	2	0.06
4218	Diane Robbins	1	1	13	2	0.06
4219	Fang Liao	1	1	13	2	0.06
4220	Fatma Henck-Weinhold	1	1	13	2	0.06
4221	Godofredo Franch Sanabria	1	1	13	2	0.06
4222	Herr Henry Grein Groth	1	1	13	2	0.06
4223	James Murray	1	1	13	2	0.06
4224	Jessica Beck	1	1	13	2	0.06
4225	Jose Harmon	1	1	13	2	0.06
4226	Kaori Kobayashi	1	1	13	2	0.06
4227	Kenneth Miller	1	1	13	2	0.06
4228	Lei Mao	1	1	13	2	0.06
4229	Lindsey Terry	1	1	13	2	0.06
4230	Lisa Ramos	1	1	13	2	0.06
4231	Lucas Hurley	1	1	13	2	0.06
4232	Luigina Lattuada-Carfagna	1	1	13	2	0.06
4233	Marilyn Austin	1	1	13	2	0.06
4234	Marina Giradello-Pellegrini	1	1	13	2	0.06
4235	Matilda Gottardi	1	1	13	2	0.06
4236	Raffaele Cantimori-Nicolini	1	1	13	2	0.06
4237	Raymond de la Lebrun	1	1	13	2	0.06
4238	Roswita Weinhold	1	1	13	2	0.06
4239	Stefani Magrassi	1	1	13	2	0.06
4240	Tiziano Bianchi	1	1	13	2	0.06
4241	Vincenza Tropea	1	1	13	2	0.06
4242	Wei Lai	1	1	13	2	0.06
4243	Xia Liu	1	1	13	2	0.06
4244	Yui Kondo	1	1	13	2	0.06
4245	Zachary Estes	1	1	13	2	0.06
4246	Zachary Woods	1	1	13	2	0.06
4247	Aria Stone	2	1	4	0	0.06
4248	Ash Mercer	2	1	4	0	0.06
4249	Audrey Huff	2	1	4	0	0.06
4250	Green Haze Collective	2	1	4	0	0.06
4251	Jax Holloway	2	1	4	0	0.06
4252	Jaxon Rivers	2	1	4	0	0.06
4253	Lila Green	2	1	4	0	0.06
4254	Riley Vex	2	1	4	0	0.06
4255	Theo Gray	2	1	4	0	0.06
4256	Bartek Krzysiek	1	1	9	3	0.06
4257	Charles Brown	1	1	9	3	0.06
4258	Franck Gillet	1	1	9	3	0.06
4259	Gertrude Dietz-Drub	1	1	9	3	0.06
4260	Gokul Konda	1	1	9	3	0.06
4261	Hanna Aelftrud van Wessex	1	1	9	3	0.06
4262	Imran de Bruijn-Hendrikse	1	1	9	3	0.06
4263	Krista Fritsch	1	1	9	3	0.06
4264	Michael Hart	1	1	9	3	0.06
4265	Michelle Moore	1	1	9	3	0.06
4266	Prerak Keer	1	1	9	3	0.06
4267	Rocco Hauffer	1	1	9	3	0.06
4268	Thilo Sölzer	1	1	9	3	0.06
4269	Xiulan Xiong	1	1	9	3	0.06
4270	Kimberly Weaver	1	1	5	4	0.06
4271	Alan Mcclain	1	1	16	1	0.06
4272	Andrew Medina	1	1	16	1	0.06
4273	Ashley Williams	1	1	16	1	0.06
4274	Brianna Logan	1	1	16	1	0.06
4275	Brianna Tucker	1	1	16	1	0.06
4276	Christina Berry	1	1	16	1	0.06
4277	Debra Bates	1	1	16	1	0.06
4278	Franca Wilms	1	1	16	1	0.06
4279	Haley Walls	1	1	16	1	0.06
4280	Jake Casey	1	1	16	1	0.06
4281	Jeffery Koch	1	1	16	1	0.06
4282	Joseph Crawford	1	1	16	1	0.06
4283	Justin Mcclain	1	1	16	1	0.06
4284	Kelly Sweeney	1	1	16	1	0.06
4285	Matthew Hickman	1	1	16	1	0.06
4286	Melanie Barker	1	1	16	1	0.06
4287	Nicole Gray	1	1	16	1	0.06
4288	Rebecca Mcneil	1	1	16	1	0.06
4289	Richard Richards	1	1	16	1	0.06
4290	Rodney Hurst	1	1	16	1	0.06
4291	Zachary Stark	1	1	16	1	0.06
4292	Adrienne Bouchet	1	1	12	2	0.06
4293	Akira Saito	1	1	12	2	0.06
4294	Alex Moore	1	1	12	2	0.06
4295	Alfons Rudolph-Holzapfel	1	1	12	2	0.06
4296	Amanda Brown	1	1	12	2	0.06
4297	Amanda Sullivan	1	1	12	2	0.06
4298	Anthony Allen	1	1	12	2	0.06
4299	Brandon Kirby	1	1	12	2	0.06
4300	Brian Carter	1	1	12	2	0.06
4301	Brittany Moreno	1	1	12	2	0.06
4302	Caroline Navarro	1	1	12	2	0.06
4303	Christine Martin	1	1	12	2	0.06
4304	Clemens Henck	1	1	12	2	0.06
4305	Cécile Meyer	1	1	12	2	0.06
4306	Diether Lachmann	1	1	12	2	0.06
4307	Edward Clark	1	1	12	2	0.06
4308	Ernestine Killer	1	1	12	2	0.06
4309	Francesco Lachmann-Ruppert	1	1	12	2	0.06
4310	Frédérique Traore	1	1	12	2	0.06
4311	Grant Coleman	1	1	12	2	0.06
4312	Gregory Frost	1	1	12	2	0.06
4313	Hans-J. Hendriks	1	1	12	2	0.06
4314	Herr Robin Drewes	1	1	12	2	0.06
4315	Isaac Lamb	1	1	12	2	0.06
4316	James Rodriguez	1	1	12	2	0.06
4317	James Smith	1	1	12	2	0.06
4318	John Shaw	1	1	12	2	0.06
4319	Johnny Walker	1	1	12	2	0.06
4320	Juan Liao	1	1	12	2	0.06
4321	Kenneth Lee	1	1	12	2	0.06
4322	Kristin Sullivan	1	1	12	2	0.06
4323	Laura Mann	1	1	12	2	0.06
4324	Lei Yin	1	1	12	2	0.06
4325	Lioba Vollbrecht	1	1	12	2	0.06
4326	Lisa Peterson	1	1	12	2	0.06
4327	Luciano Chindamo	1	1	12	2	0.06
4328	Marlene Juncken	1	1	12	2	0.06
4329	Mikako Kondo	1	1	12	2	0.06
4330	Miss Brandi Torres	1	1	12	2	0.06
4331	Momoko Yamamoto	1	1	12	2	0.06
4332	Osamu Hayashi	1	1	12	2	0.06
4333	Richard Reyes	1	1	12	2	0.06
4334	Robert Boone	1	1	12	2	0.06
4335	Ruth Hernandez	1	1	12	2	0.06
4336	Sandra Burke	1	1	12	2	0.06
4337	Sarah Rivera	1	1	12	2	0.06
4338	Satomi Aoki	1	1	12	2	0.06
4339	Shelby Campbell	1	1	12	2	0.06
4340	Stacey Armstrong	1	1	12	2	0.06
4341	Steve Young	1	1	12	2	0.06
4342	Tao Lu	1	1	12	2	0.06
4343	Trevor Bass	1	1	12	2	0.06
4344	Viktor Kreusel	1	1	12	2	0.06
4345	William Miller	1	1	12	2	0.06
4346	Xia Cheng	1	1	12	2	0.06
4347	Yosuke Ito	1	1	12	2	0.06
4348	Iris Moon	2	1	3	0	0.06
4349	Luna Skye	2	1	3	0	0.06
4350	Max Wilder	2	1	3	0	0.06
4351	Nova Blaze	2	1	3	0	0.06
4352	Zane Cruz	2	1	3	0	0.06
4353	Adam Knox	1	1	8	3	0.06
4354	Anaya Butala	1	1	8	3	0.06
4355	Caleb Payne	1	1	8	3	0.06
4356	Christy Hernandez	1	1	8	3	0.06
4357	Elizabeth Dobes	1	1	8	3	0.06
4358	Igor Rychcik	1	1	8	3	0.06
4359	Jeffrey Ward	1	1	8	3	0.06
4360	Jeremy Alvarez	1	1	8	3	0.06
4361	John Campbell	1	1	8	3	0.06
4362	Joseph Long	1	1	8	3	0.06
4363	Lunar Prism	1	1	8	3	0.06
4364	Erik Alvarez	1	1	19	0	0.06
4365	Gary White	1	1	19	0	0.06
4366	Joel Valentine	1	1	19	0	0.06
4367	Joseph Buchanan	1	1	19	0	0.06
4368	Julie Graves	1	1	19	0	0.06
4369	Karen Smith	1	1	19	0	0.06
4370	Mario Weeks	1	1	19	0	0.06
4371	Mary Ross	1	1	19	0	0.06
4372	Nicholas Ortiz	1	1	19	0	0.06
4373	Nicole Williams	1	1	19	0	0.06
4374	Patrick Thompson	1	1	19	0	0.06
4375	Robert Holland	1	1	19	0	0.06
4376	Shawn Soto	1	1	19	0	0.06
4377	Xavier Kane	1	1	19	0	0.06
4378	Amanda Byrd	1	1	15	1	0.06
4379	Evelyn Banks	1	1	15	1	0.06
4380	Frederick Hicks	1	1	15	1	0.06
4381	Gregory Patton	1	1	15	1	0.06
4382	Isabella Smith	1	1	15	1	0.06
4383	Kari Warren	1	1	15	1	0.06
4384	Linus Bonbach	1	1	15	1	0.06
4385	Lisa Harris	1	1	15	1	0.06
4386	Raimund Wernecke	1	1	15	1	0.06
4387	Sabrina Jenkins	1	1	15	1	0.06
4388	Sarah Banks	1	1	15	1	0.06
4389	Sean Harrison	1	1	15	1	0.06
4390	Shelby Contreras	1	1	15	1	0.06
4391	Xiulan Guo	1	1	15	1	0.06
4392	Yang Qin	1	1	15	1	0.06
4393	Alyssa Thompson	1	1	11	2	0.06
4394	Anika Behl	1	1	11	2	0.06
4395	Anneliese Döhn	1	1	11	2	0.06
4396	Benjamin Brown	1	1	11	2	0.06
4397	Benjamin Lee	1	1	11	2	0.06
4398	Bradley Hamilton	1	1	11	2	0.06
4399	Carlos Brown	1	1	11	2	0.06
4400	Chris Tate	1	1	11	2	0.06
4401	Cosima Mülichen	1	1	11	2	0.06
4402	Deborah Wright	1	1	11	2	0.06
4403	Eric Williams	1	1	11	2	0.06
4404	Eva Seidel	1	1	11	2	0.06
4405	Fenna Zwart	1	1	11	2	0.06
4406	Gerhild Patberg	1	1	11	2	0.06
4407	Hilda Plath	1	1	11	2	0.06
4408	Jasmine Lopez	1	1	11	2	0.06
4409	Jennifer Brown	1	1	11	2	0.06
4410	Jeremi Pronobis	1	1	11	2	0.06
4411	Jing Wen	1	1	11	2	0.06
4412	Jo Barnes	1	1	11	2	0.06
4413	Jonathan Williams	1	1	11	2	0.06
4414	Julia Jones	1	1	11	2	0.06
4415	Kari Miranda	1	1	11	2	0.06
4416	Kathleen Sandoval	1	1	11	2	0.06
4417	Keith Clark	1	1	11	2	0.06
4418	Kevin Ruiz	1	1	11	2	0.06
4419	Kristin Barber	1	1	11	2	0.06
4420	Ludger Ladeck	1	1	11	2	0.06
4421	Max Howard	1	1	11	2	0.06
4422	Melissa Dennis	1	1	11	2	0.06
4423	Michael Anderson	1	1	11	2	0.06
4424	Michelle Shepard	1	1	11	2	0.06
4425	Miranda Gonzales	1	1	11	2	0.06
4426	Nakul Agarwal	1	1	11	2	0.06
4427	Nataniel Nazarewicz	1	1	11	2	0.06
4428	Ninetta Spinelli	1	1	11	2	0.06
4429	Paloma Tomaselli	1	1	11	2	0.06
4430	Raymond Wright	1	1	11	2	0.06
4431	Richard Kennedy	1	1	11	2	0.06
4432	Robin Garrett	1	1	11	2	0.06
4433	Ronald Marshall	1	1	11	2	0.06
4434	Róża Bełz	1	1	11	2	0.06
4435	Sabrina Jimenez	1	1	11	2	0.06
4436	Sahil Basak	1	1	11	2	0.06
4437	Stephanie Frank	1	1	11	2	0.06
4438	Sylvia Thompson	1	1	11	2	0.06
4439	Taylor Reed	1	1	11	2	0.06
4440	Valentine Peukert	1	1	11	2	0.06
4441	Valerie Miller	1	1	11	2	0.06
4442	Yang Hao	1	1	11	2	0.06
4443	Benjamin Clark	3	0	3	0	0.06
4444	Maximus Grey	3	0	3	0	0.06
4445	Sophia Brooks	3	0	3	0	0.06
4446	Quinn Rivers	2	1	2	0	0.06
4447	Ryder Lyons	2	1	2	0	0.06
4448	Xiuying Tan	2	1	2	0	0.06
4449	Lunar Echo	1	1	7	3	0.06
4450	Maaya Ishikawa	1	1	7	3	0.06
4451	Paul Dunn	1	1	7	3	0.06
4452	Sayuri Kondo	1	1	7	3	0.06
4453	Torsten Etzold	1	1	7	3	0.06
4454	Veronica Bell	1	1	7	3	0.06
4455	Yui Ito	1	1	7	3	0.06
4456	Amy Powell	1	1	14	1	0.06
4457	Angela Thomas	1	1	14	1	0.06
4458	Angela Villa	1	1	14	1	0.06
4459	Carol Simpson	1	1	14	1	0.06
4460	Cassidy Hubbard	1	1	14	1	0.06
4461	Debra Graham	1	1	14	1	0.06
4462	Donna Hanson	1	1	14	1	0.06
4463	Dylan Schwartz	1	1	14	1	0.06
4464	Edward Evans	1	1	14	1	0.06
4465	Erika Ruiz	1	1	14	1	0.06
4466	Heather Cabrera	1	1	14	1	0.06
4467	James Aguilar	1	1	14	1	0.06
4468	James Henderson	1	1	14	1	0.06
4469	Joel Long	1	1	14	1	0.06
4470	Jörg-Peter Hecker	1	1	14	1	0.06
4471	Katherine Johnston	1	1	14	1	0.06
4472	Kelly Castro	1	1	14	1	0.06
4473	Laura Gordon	1	1	14	1	0.06
4474	Melissa Arias	1	1	14	1	0.06
4475	Michael Nixon	1	1	14	1	0.06
4476	Midnight Prism	1	1	14	1	0.06
4477	Milica Textor-Sorgatz	1	1	14	1	0.06
4478	Nathan Jones	1	1	14	1	0.06
4479	Oliver Henschel	1	1	14	1	0.06
4480	Patrick Cook	1	1	14	1	0.06
4481	Ryan Leblanc	1	1	14	1	0.06
4482	Shawn Johnson	1	1	14	1	0.06
4483	Stanley Hamilton	1	1	14	1	0.06
4484	Stephen Lawrence	1	1	14	1	0.06
4485	Susan Christian	1	1	14	1	0.06
4486	Terri Reynolds	1	1	14	1	0.06
4487	Ayse Häring	1	1	10	2	0.06
4488	Christina Smith	1	1	10	2	0.06
4489	Courtney Bennett	1	1	10	2	0.06
4490	Gisa Hettner-Adler	1	1	10	2	0.06
4491	Gloria Gutknecht	1	1	10	2	0.06
4492	Jie Han	1	1	10	2	0.06
4493	Jose Garrett	1	1	10	2	0.06
4494	Lori Martin	1	1	10	2	0.06
4495	Pierre Ruppert	1	1	10	2	0.06
4496	Qiang Hu	1	1	10	2	0.06
4497	Robert Combs	1	1	10	2	0.06
4498	The Omega Collective	1	1	10	2	0.06
4499	Theodore Shaw	1	1	10	2	0.06
4500	William Tyler	1	1	10	2	0.06
4501	Xia Zou	1	1	10	2	0.06
4502	Antoinette Bertin	1	1	6	3	0.06
4503	Cynthia Hayes	1	1	6	3	0.06
4504	Kimberly Keller	1	1	6	3	0.06
4505	Ping Zhu	1	1	6	3	0.06
4506	Ronnie Rodriguez	1	1	6	3	0.06
4507	Yang Tian	1	1	6	3	0.06
4508	Alfredo del Pujol	1	1	13	1	0.06
4509	Amancio Delgado Morata	1	1	13	1	0.06
4510	Amanda Reyes	1	1	13	1	0.06
4511	Antonio Allen	1	1	13	1	0.06
4512	Ariel Falcó Narváez	1	1	13	1	0.06
4513	Arthur Colas	1	1	13	1	0.06
4514	Bartolomeo Castellitto-Gritti	1	1	13	1	0.06
4515	Benvenuto Castiglione-Pulci	1	1	13	1	0.06
4516	Borja Santamaria-Adán	1	1	13	1	0.06
4517	Corinna Schweitzer	1	1	13	1	0.06
4518	Daniel Martin	1	1	13	1	0.06
4519	Dylan Williams	1	1	13	1	0.06
4520	Elizabeth Adams	1	1	13	1	0.06
4521	Erin Hall	1	1	13	1	0.06
4522	Eveline Gotthard	1	1	13	1	0.06
4523	Fabiana Narváez Vazquez	1	1	13	1	0.06
4524	Gang Shi	1	1	13	1	0.06
4525	Geronimo Lamborghini-Dossetti	1	1	13	1	0.06
4526	Gioele Merisi	1	1	13	1	0.06
4527	Giuliano Anastasi	1	1	13	1	0.06
4528	Hans-Karl Schottin	1	1	13	1	0.06
4529	Hermann Josef Matthäi	1	1	13	1	0.06
4530	Jennifer Curtis	1	1	13	1	0.06
4531	Jessica Phelps	1	1	13	1	0.06
4532	Jessica Smith	1	1	13	1	0.06
4533	Joseph Nichols	1	1	13	1	0.06
4534	Kara Rich	1	1	13	1	0.06
4535	Katherine Olson	1	1	13	1	0.06
4536	Kati Sauer-Rudolph	1	1	13	1	0.06
4537	Kim Washington	1	1	13	1	0.06
4538	Krista Dunn	1	1	13	1	0.06
4539	Luciano Maderna	1	1	13	1	0.06
4540	Mandy Hood	1	1	13	1	0.06
4541	Marc Ward	1	1	13	1	0.06
4542	Maria Morris	1	1	13	1	0.06
4543	Matthew Walker	1	1	13	1	0.06
4544	Mayte Salvà Durán	1	1	13	1	0.06
4545	Melanie Armstrong	1	1	13	1	0.06
4546	Melissa Merritt	1	1	13	1	0.06
4547	Michelle Anderson	1	1	13	1	0.06
4548	Mohamed Cazorla	1	1	13	1	0.06
4549	Naoko Miura	1	1	13	1	0.06
4550	Nico Castellanos García	1	1	13	1	0.06
4551	Paola Condoleo	1	1	13	1	0.06
4552	Paula Mendizábal Alegria	1	1	13	1	0.06
4553	Renata Parpinel	1	1	13	1	0.06
4554	Robert Villanueva	1	1	13	1	0.06
4555	Rodolfo Cutuli	1	1	13	1	0.06
4556	Rosario Pujadas Barceló	1	1	13	1	0.06
4557	Sharon Crawford	1	1	13	1	0.06
4558	Silvio Frías Castejón	1	1	13	1	0.06
4559	Stephanie House	1	1	13	1	0.06
4560	Terri Thomas	1	1	13	1	0.06
4561	Wei Yang	1	1	13	1	0.06
4562	Yang Mao	1	1	13	1	0.06
4563	Alexandra Moreno	1	1	9	2	0.06
4564	Amico Luciani	1	1	9	2	0.06
4565	Anna Vávrová	1	1	9	2	0.06
4566	Ashley Robinson	1	1	9	2	0.06
4567	Caleb Ross	1	1	9	2	0.06
4568	Christine Turner	1	1	9	2	0.06
4569	Courtney Castillo	1	1	9	2	0.06
4570	David Reese	1	1	9	2	0.06
4571	David White	1	1	9	2	0.06
4572	Elmo Calbo	1	1	9	2	0.06
4573	Gary Stephens	1	1	9	2	0.06
4574	Jennifer Davis	1	1	9	2	0.06
4575	Jun Tao	1	1	9	2	0.06
4576	Karsten Löwer	1	1	9	2	0.06
4577	Katie Oliver	1	1	9	2	0.06
4578	Keith Stone	1	1	9	2	0.06
4579	Kelli Jackson	1	1	9	2	0.06
4580	Li Dai	1	1	9	2	0.06
4581	Mackenzie Greene	1	1	9	2	0.06
4582	Mary Combs	1	1	9	2	0.06
4583	Matthieu-Daniel Launay	1	1	9	2	0.06
4584	Megan Bridges	1	1	9	2	0.06
4585	Nancy Mitchell	1	1	9	2	0.06
4586	Nicholas Porter	1	1	9	2	0.06
4587	Nick Kensy	1	1	9	2	0.06
4588	Nicolas Jones	1	1	9	2	0.06
4589	Nicole Bass	1	1	9	2	0.06
4590	Richard Gonzales	1	1	9	2	0.06
4591	Richard Kovář	1	1	9	2	0.06
4592	Ronnie Watkins	1	1	9	2	0.06
4593	Ryan Dunlap	1	1	9	2	0.06
4594	Saira Sood	1	1	9	2	0.06
4595	Sean King	1	1	9	2	0.06
4596	Shelly Lewis	1	1	9	2	0.06
4597	Stefanie Sullivan	1	1	9	2	0.06
4598	Thérèse Guillou	1	1	9	2	0.06
4599	Tristan Chauveau	1	1	9	2	0.06
4600	Warren Fleming	1	1	9	2	0.06
4601	Xiuying Jia	1	1	9	2	0.06
4602	Yves Aubry	1	1	9	2	0.06
4603	Bianca Singleton	1	1	5	3	0.06
4604	Brianna Norris	1	1	5	3	0.06
4605	Elias Rouwenhorst	1	1	5	3	0.06
4606	Erin Smith	1	1	5	3	0.06
4607	Igor Dyś	1	1	5	3	0.06
4608	Jérôme Riou	1	1	5	3	0.06
4609	Michelle Laurent de la Berger	1	1	5	3	0.06
4610	Nicholas Burns	1	1	5	3	0.06
4611	Teresa Bohlander	1	1	5	3	0.06
4612	Aaron Livingston	1	1	12	1	0.05
4613	Aleksandar Kohl	1	1	12	1	0.05
4614	Anay Behl	1	1	12	1	0.05
4615	Angelica Reid	1	1	12	1	0.05
4616	Atenulf Nadi-Vergassola	1	1	12	1	0.05
4617	Brittany Mueller	1	1	12	1	0.05
4618	Cassandra Carroll	1	1	12	1	0.05
4619	Chad Chavez	1	1	12	1	0.05
4620	David Duncan	1	1	12	1	0.05
4621	Emily Sullivan	1	1	12	1	0.05
4622	Eric Morrison	1	1	12	1	0.05
4623	Fabrizia Gualtieri	1	1	12	1	0.05
4624	Folker Jacob-Kreusel	1	1	12	1	0.05
4625	Francis Kaufman	1	1	12	1	0.05
4626	Francisco Kerr	1	1	12	1	0.05
4627	Gail Garrett	1	1	12	1	0.05
4628	Hanako Takahashi	1	1	12	1	0.05
4629	Holly Young	1	1	12	1	0.05
4630	Jie Qin	1	1	12	1	0.05
4631	Johanne Salz	1	1	12	1	0.05
4632	Joseph Preston	1	1	12	1	0.05
4633	Joshua Cobb	1	1	12	1	0.05
4634	Kana Hashimoto	1	1	12	1	0.05
4635	Kimberly Martin	1	1	12	1	0.05
4636	Maria-Luise Flantz	1	1	12	1	0.05
4637	Marissa Holt	1	1	12	1	0.05
4638	Matthew Adams	1	1	12	1	0.05
4639	Michael Hill	1	1	12	1	0.05
4640	Miss Stephanie Middleton	1	1	12	1	0.05
4641	Monique Jefferson	1	1	12	1	0.05
4642	Nidia Menéndez Mateo	1	1	12	1	0.05
4643	Pedro Scholtz	1	1	12	1	0.05
4644	Rachael Stafford	1	1	12	1	0.05
4645	Richard White	1	1	12	1	0.05
4646	Robert Cowan	1	1	12	1	0.05
4647	Sarah Phillips	1	1	12	1	0.05
4648	Sarah Wright	1	1	12	1	0.05
4649	Shamik Jhaveri	1	1	12	1	0.05
4650	Tracey Murphy	1	1	12	1	0.05
4651	Vitus Heinz	1	1	12	1	0.05
4652	William Chan	1	1	12	1	0.05
4653	Zara Dugar	1	1	12	1	0.05
4654	Andrew Peters	1	1	8	2	0.05
4655	Audrey Le Boucher	1	1	8	2	0.05
4656	Benoît Delmas	1	1	8	2	0.05
4657	Donald Cooper	1	1	8	2	0.05
4658	Erika Austin	1	1	8	2	0.05
4659	Henny Caspar	1	1	8	2	0.05
4660	Justus Hornig	1	1	8	2	0.05
4661	Kornelia Pędzik	1	1	8	2	0.05
4662	Manon Diaz	1	1	8	2	0.05
4663	Rika Tanaka	1	1	8	2	0.05
4664	Robert Johnson	1	1	8	2	0.05
4665	Travis Gonzales	1	1	8	2	0.05
4666	Melissa Lucero	1	0	14	5	0.05
4667	Thomas Lee	1	0	14	5	0.05
4668	Vít Svoboda	1	0	14	5	0.05
4669	Daisy Rose	1	0	25	2	0.05
4670	Mark West	1	0	25	2	0.05
4671	Sara Johnson	1	0	25	2	0.05
4672	Stephanie Andersen	1	0	25	2	0.05
4673	Tiffany Simmons	1	0	25	2	0.05
4674	Aaron Young	1	1	15	0	0.05
4675	Benjamin Moore	1	1	15	0	0.05
4676	Connor Lopez	1	1	15	0	0.05
4677	Fang Kang	1	1	15	0	0.05
4678	Haley Joseph	1	1	15	0	0.05
4679	Hannah Roberson	1	1	15	0	0.05
4680	Mike Rose	1	1	15	0	0.05
4681	Ming Yin	1	1	15	0	0.05
4682	Robert Rollins	1	1	15	0	0.05
4683	Robin Noble	1	1	15	0	0.05
4684	Timothy Scott	1	1	15	0	0.05
4685	Alexis Hawkins	1	1	11	1	0.05
4686	Alfio Greggio	1	1	11	1	0.05
4687	Alina Ricciardi	1	1	11	1	0.05
4688	Andrea Martin	1	1	11	1	0.05
4689	Angel Henderson	1	1	11	1	0.05
4690	Ashley Waters	1	1	11	1	0.05
4691	Atenulf Pigafetta	1	1	11	1	0.05
4692	Audrey King	1	1	11	1	0.05
4693	Bobby Guzman	1	1	11	1	0.05
4694	Bruno Silvestri	1	1	11	1	0.05
4695	Calixta Moya Gómez	1	1	11	1	0.05
4696	Christopher Aguilar	1	1	11	1	0.05
4697	Cipriano Navone	1	1	11	1	0.05
4698	Craig Ortega	1	1	11	1	0.05
4699	Danielle Martin	1	1	11	1	0.05
4700	Devin Mccoy	1	1	11	1	0.05
4701	Erin Kim	1	1	11	1	0.05
4702	Erin Moses	1	1	11	1	0.05
4703	Eva Squarcione	1	1	11	1	0.05
4704	Fang Zhao	1	1	11	1	0.05
4705	Gilberto Cainero	1	1	11	1	0.05
4706	Hannah Garrett	1	1	11	1	0.05
4707	Heather Miller	1	1	11	1	0.05
4708	James Torres	1	1	11	1	0.05
4709	Jing Cheng	1	1	11	1	0.05
4710	Jing Fan	1	1	11	1	0.05
4711	Jing Zeng	1	1	11	1	0.05
4712	Jonathan Vasquez	1	1	11	1	0.05
4713	Jose White	1	1	11	1	0.05
4714	Juanito Jimenez Hernández	1	1	11	1	0.05
4715	Kaitlin Davis	1	1	11	1	0.05
4716	Kaori Ito	1	1	11	1	0.05
4717	Katrina Maynard	1	1	11	1	0.05
4718	Kenneth Johnson	1	1	11	1	0.05
4719	Kimberly Miles	1	1	11	1	0.05
4720	Kristine Bullock	1	1	11	1	0.05
4721	Li Yan	1	1	11	1	0.05
4722	Luke Murray	1	1	11	1	0.05
4723	Manfred Schmiedecke	1	1	11	1	0.05
4724	Mariana Galeati	1	1	11	1	0.05
4725	Mary Anthony	1	1	11	1	0.05
4726	Micheal Mcguire	1	1	11	1	0.05
4727	Na Gong	1	1	11	1	0.05
4728	Naoko Sato	1	1	11	1	0.05
4729	Olivia Ortega Bermejo	1	1	11	1	0.05
4730	Qiang Yao	1	1	11	1	0.05
4731	Rachel Curry	1	1	11	1	0.05
4732	Raffaello Tarchetti	1	1	11	1	0.05
4733	Samantha Andrews	1	1	11	1	0.05
4734	Samantha Dawson	1	1	11	1	0.05
4735	Sandy Ray	1	1	11	1	0.05
4736	Susan Young	1	1	11	1	0.05
4737	Virginia Ford	1	1	11	1	0.05
4738	Xiuying Lei	1	1	11	1	0.05
4739	Yoko Hashimoto	1	1	11	1	0.05
4740	Adrien Gonzalez-Duval	1	1	7	2	0.05
4741	Alessandra Omma-Paolini	1	1	7	2	0.05
4742	Alexis Griffith	1	1	7	2	0.05
4743	Amanda Kennedy	1	1	7	2	0.05
4744	Anastazja Brózda	1	1	7	2	0.05
4745	Andrea Hamilton	1	1	7	2	0.05
4746	Andrzej Kamyszek	1	1	7	2	0.05
4747	Angela Fisher	1	1	7	2	0.05
4748	Antoine Blanc	1	1	7	2	0.05
4749	Audrey Picard	1	1	7	2	0.05
4750	Carrie Lee	1	1	7	2	0.05
4751	Christina Cunningham	1	1	7	2	0.05
4752	Ehrenfried Werner	1	1	7	2	0.05
4753	Erik Sharp	1	1	7	2	0.05
4754	Felicia Gilbert	1	1	7	2	0.05
4755	Franciszek Wieja	1	1	7	2	0.05
4756	Frederick Houston	1	1	7	2	0.05
4757	Guy Arnaud	1	1	7	2	0.05
4758	Hannah Newton	1	1	7	2	0.05
4759	Hellmut Graf	1	1	7	2	0.05
4760	Jivika Dar	1	1	7	2	0.05
4761	Kelly Carpenter	1	1	7	2	0.05
4762	Kimberly Howe	1	1	7	2	0.05
4763	Lauren Brown	1	1	7	2	0.05
4764	Lauren Turner	1	1	7	2	0.05
4765	Marcelina Wochnik	1	1	7	2	0.05
4766	Mary Frank	1	1	7	2	0.05
4767	Maryse-Margaux Pinto	1	1	7	2	0.05
4768	Megan Franklin	1	1	7	2	0.05
4769	Michael Jacobson	1	1	7	2	0.05
4770	Nancy Dunlap	1	1	7	2	0.05
4771	Natalie Fields	1	1	7	2	0.05
4772	Nicole Lee	1	1	7	2	0.05
4773	Paul Hughes	1	1	7	2	0.05
4774	Rico Weimer	1	1	7	2	0.05
4775	Rika Shimizu	1	1	7	2	0.05
4776	Robert Velasquez	1	1	7	2	0.05
4777	Sandra Hunter	1	1	7	2	0.05
4778	Shannon White	1	1	7	2	0.05
4779	Stephanie Allen	1	1	7	2	0.05
4780	Theresa Webster	1	1	7	2	0.05
4781	Todd Lopez	1	1	7	2	0.05
4782	Whitney Yates	1	1	7	2	0.05
4783	Andrew Coleman	1	1	14	0	0.05
4784	Austin Robinson	1	1	14	0	0.05
4785	Carlo Altera	1	1	14	0	0.05
4786	Dariusz Trupp	1	1	14	0	0.05
4787	Emilio Missoni	1	1	14	0	0.05
4788	Federigo Satta	1	1	14	0	0.05
4789	Felicia Walton	1	1	14	0	0.05
4790	Giovanna Tirabassi	1	1	14	0	0.05
4791	Hugo Visconti	1	1	14	0	0.05
4792	Jaroslav Putz	1	1	14	0	0.05
4793	Jens-Uwe Pruschke	1	1	14	0	0.05
4794	Joe Humphrey	1	1	14	0	0.05
4795	Kathleen Sutton	1	1	14	0	0.05
4796	Kayla Gonzales	1	1	14	0	0.05
4797	Lorenzo Baresi	1	1	14	0	0.05
4798	Ludmila Wesack	1	1	14	0	0.05
4799	Maria Zola	1	1	14	0	0.05
4800	Nicholas Swanson	1	1	14	0	0.05
4801	Olivia Steinberg	1	1	14	0	0.05
4802	Patrick Howard	1	1	14	0	0.05
4803	Susan Scott	1	1	14	0	0.05
4804	Terri Chapman	1	1	14	0	0.05
4805	Vanessa Villadicani	1	1	14	0	0.05
4806	Alejandro Barnes	1	1	10	1	0.05
4807	Alexa Brown	1	1	10	1	0.05
4808	Alexander Budig	1	1	10	1	0.05
4809	Aniela Chojna	1	1	10	1	0.05
4810	Ashley Bennett	1	1	10	1	0.05
4811	Betty Faulkner	1	1	10	1	0.05
4812	Brian Anderson	1	1	10	1	0.05
4813	Charles Paul	1	1	10	1	0.05
4814	Chase Andrews	1	1	10	1	0.05
4815	Christopher Wilcox	1	1	10	1	0.05
4816	Christy Lewis	1	1	10	1	0.05
4817	Daniel Cross	1	1	10	1	0.05
4818	Daniel Miller	1	1	10	1	0.05
4819	David Glenn	1	1	10	1	0.05
4820	Delia Hesse-Wulf	1	1	10	1	0.05
4821	Edwin Diaz	1	1	10	1	0.05
4822	Eric Wallace	1	1	10	1	0.05
4823	Gang Ding	1	1	10	1	0.05
4824	Guenter Stumpf	1	1	10	1	0.05
4825	Herr Rudolph Schaaf	1	1	10	1	0.05
4826	Jacob Grimes	1	1	10	1	0.05
4827	James Cruz	1	1	10	1	0.05
4828	James Vaughn	1	1	10	1	0.05
4829	James White	1	1	10	1	0.05
4830	Janet Cunningham	1	1	10	1	0.05
4831	Jasmine Hammond	1	1	10	1	0.05
4832	Johnny Smith	1	1	10	1	0.05
4833	Jorge Brown	1	1	10	1	0.05
4834	Justin Allen	1	1	10	1	0.05
4835	Karen Schultz	1	1	10	1	0.05
4836	Kevin Simmons	1	1	10	1	0.05
4837	Kimberly Knight	1	1	10	1	0.05
4838	Kirk Griffin	1	1	10	1	0.05
4839	Kyle Myers	1	1	10	1	0.05
4840	Linda Chase	1	1	10	1	0.05
4841	Lonnie Estrada	1	1	10	1	0.05
4842	Louis Brown	1	1	10	1	0.05
4843	Mark Stokes	1	1	10	1	0.05
4844	Mehmet Junk	1	1	10	1	0.05
4845	Melody Cuevas	1	1	10	1	0.05
4846	Michael Frank	1	1	10	1	0.05
4847	Michael Pearson	1	1	10	1	0.05
4848	Michael Williamson	1	1	10	1	0.05
4849	Nicholas Moss	1	1	10	1	0.05
4850	Rachel Rios	1	1	10	1	0.05
4851	Rainer Mans	1	1	10	1	0.05
4852	Ryan Baxter	1	1	10	1	0.05
4853	Sami Bohnbach	1	1	10	1	0.05
4854	Sarah Benson	1	1	10	1	0.05
4855	Sonia Porzio	1	1	10	1	0.05
4856	Stanley Roberts	1	1	10	1	0.05
4857	Tammie Johnson	1	1	10	1	0.05
4858	Tao Tan	1	1	10	1	0.05
4859	Temistocle Vivaldi-Amato	1	1	10	1	0.05
4860	Thomas Leon	1	1	10	1	0.05
4861	Todd Mcbride	1	1	10	1	0.05
4862	Wei Song	1	1	10	1	0.05
4863	Whisper Cascade	1	1	10	1	0.05
4864	William Vollbrecht	1	1	10	1	0.05
4865	Wiltrud Hübel	1	1	10	1	0.05
4866	Yan Mao	1	1	10	1	0.05
4867	Yan Pan	1	1	10	1	0.05
4868	Yang Shen	1	1	10	1	0.05
4869	Yvonne Powell	1	1	10	1	0.05
4870	Amanda Frazier	1	1	6	2	0.05
4871	Anthony Farmer	1	1	6	2	0.05
4872	Austin Martin	1	1	6	2	0.05
4873	Beverly Powell	1	1	6	2	0.05
4874	Cody Rowe	1	1	6	2	0.05
4875	Debra Anderson	1	1	6	2	0.05
4876	Grzegorz Rorat	1	1	6	2	0.05
4877	James Mcmahon	1	1	6	2	0.05
4878	Jeffrey Hill	1	1	6	2	0.05
4879	John Sanchez	1	1	6	2	0.05
4880	Kenichi Matsuda	1	1	6	2	0.05
4881	Maite Cuevas Caballero	1	1	6	2	0.05
4882	Milena Anichini-Barracco	1	1	6	2	0.05
4883	Nicolas Marsh	1	1	6	2	0.05
4884	Samantha Hughes	1	1	6	2	0.05
4885	Shannon Oneill	1	1	6	2	0.05
4886	Steven Medina	1	1	6	2	0.05
4887	Tao Wu	1	1	6	2	0.05
4888	Tomoya Shimizu	1	1	6	2	0.05
4889	Travis Tucker	1	1	6	2	0.05
4890	Tomoya Kato	1	1	2	3	0.05
4891	Emily Perkins	1	1	13	0	0.05
4892	John Johnston	1	1	13	0	0.05
4893	Joseph Rodriguez	1	1	13	0	0.05
4894	Kimberly Lee	1	1	13	0	0.05
4895	Kristina Serrano	1	1	13	0	0.05
4896	Melissa Johnson	1	1	13	0	0.05
4897	Amy Marshall	1	1	9	1	0.05
4898	Andrea Brooks	1	1	9	1	0.05
4899	Andrew Williamson	1	1	9	1	0.05
4900	Anka Spieß	1	1	9	1	0.05
4901	Anthony Garcia	1	1	9	1	0.05
4902	Ashley Jennings	1	1	9	1	0.05
4903	Bianka Herrmann	1	1	9	1	0.05
4904	Brent Larsen	1	1	9	1	0.05
4905	Brian Morales	1	1	9	1	0.05
4906	Brooke Ortiz	1	1	9	1	0.05
4907	Carlos Mccann	1	1	9	1	0.05
4908	Casey Price	1	1	9	1	0.05
4909	Catherine Price	1	1	9	1	0.05
4910	Chelsea Paul	1	1	9	1	0.05
4911	Cheryl Brown	1	1	9	1	0.05
4912	Chiyo Suzuki	1	1	9	1	0.05
4913	Christine Jennings	1	1	9	1	0.05
4914	Christopher Cox	1	1	9	1	0.05
4915	Christopher Miller	1	1	9	1	0.05
4916	Christopher Wilson	1	1	9	1	0.05
4917	Cody Lopez	1	1	9	1	0.05
4918	Courtney Smith	1	1	9	1	0.05
4919	Crystal Yu	1	1	9	1	0.05
4920	Danielle Franklin	1	1	9	1	0.05
4921	Dennis Fields	1	1	9	1	0.05
4922	Donald Oliver	1	1	9	1	0.05
4923	Edward Matthews	1	1	9	1	0.05
4924	Elena Nibali	1	1	9	1	0.05
4925	Elenore Wulff	1	1	9	1	0.05
4926	Erica Hill	1	1	9	1	0.05
4927	Erika Martin	1	1	9	1	0.05
4928	Fabian Durys	1	1	9	1	0.05
4929	Fabienne Klemm-Kade	1	1	9	1	0.05
4930	Fang Fan	1	1	9	1	0.05
4931	Fang Zhang	1	1	9	1	0.05
4932	Frank Osborne	1	1	9	1	0.05
4933	Gioffre Garobbio	1	1	9	1	0.05
4934	Holly Horton	1	1	9	1	0.05
4935	Hubertine Plath	1	1	9	1	0.05
4936	Ida Burzec	1	1	9	1	0.05
4937	Irmtraut Benthin	1	1	9	1	0.05
4938	Jaclyn Smith	1	1	9	1	0.05
4939	James Bishop	1	1	9	1	0.05
4940	Janice Poole	1	1	9	1	0.05
4941	Jing Liu	1	1	9	1	0.05
4942	Jose Brown	1	1	9	1	0.05
4943	Joshua Shelton	1	1	9	1	0.05
4944	Juergen Carsten	1	1	9	1	0.05
4945	Justin Hensley	1	1	9	1	0.05
4946	Justin Powers	1	1	9	1	0.05
4947	Kana Mori	1	1	9	1	0.05
4948	Kaori Watanabe	1	1	9	1	0.05
4949	Karen Robinson	1	1	9	1	0.05
4950	Kathryn Smith	1	1	9	1	0.05
4951	Kenneth Evans	1	1	9	1	0.05
4952	Kristen Hamilton	1	1	9	1	0.05
4953	Kristen Porter	1	1	9	1	0.05
4954	Laura Harrison	1	1	9	1	0.05
4955	Lauren Mclean	1	1	9	1	0.05
4956	Leah Dixon	1	1	9	1	0.05
4957	Li Qin	1	1	9	1	0.05
4958	Magdalene Fliegner	1	1	9	1	0.05
4959	Manuel Wu	1	1	9	1	0.05
4960	Mark Heath	1	1	9	1	0.05
4961	Matthew Chapman	1	1	9	1	0.05
4962	Maurice Nguyen	1	1	9	1	0.05
4963	Melissa Avila	1	1	9	1	0.05
4964	Ming Dong	1	1	9	1	0.05
4965	Ming Yi	1	1	9	1	0.05
4966	Mitchell Larson	1	1	9	1	0.05
4967	Nadia Mazzanti	1	1	9	1	0.05
4968	Nicholas Bond	1	1	9	1	0.05
4969	Nicholas Lawrence	1	1	9	1	0.05
4970	Olivia Clark	1	1	9	1	0.05
4971	Peter Banks	1	1	9	1	0.05
4972	Rebecca Wheeler	1	1	9	1	0.05
4973	Renata Manolesso	1	1	9	1	0.05
4974	Robert Gonzalez	1	1	9	1	0.05
4975	Rudi Bolander-Zimmer	1	1	9	1	0.05
4976	Ryan Johnston	1	1	9	1	0.05
4977	Scott Kennedy	1	1	9	1	0.05
4978	Sophia Zaguri	1	1	9	1	0.05
4979	Stacey Bruce	1	1	9	1	0.05
4980	Stephen Thompson	1	1	9	1	0.05
4981	Summer Friedman	1	1	9	1	0.05
4982	Susan Stewart	1	1	9	1	0.05
4983	Sylvia Lawrence	1	1	9	1	0.05
4984	Taichi Sasaki	1	1	9	1	0.05
4985	Terry Harris	1	1	9	1	0.05
4986	Thomas Lopez	1	1	9	1	0.05
4987	Tim Johnson	1	1	9	1	0.05
4988	Tola Cymer	1	1	9	1	0.05
4989	Tomoya Nakamura	1	1	9	1	0.05
4990	Tomoya Takahashi	1	1	9	1	0.05
4991	Urszula Henke	1	1	9	1	0.05
4992	Wei Chen	1	1	9	1	0.05
4993	Wojciech Jarmuła	1	1	9	1	0.05
4994	Xiulan Bai	1	1	9	1	0.05
4995	Yan Dong	1	1	9	1	0.05
4996	Yong Cheng	1	1	9	1	0.05
4997	Yong Kang	1	1	9	1	0.05
4998	Yong Luo	1	1	9	1	0.05
4999	Yuki Hashimoto	1	1	9	1	0.05
5000	Olga Kalinka	1	0	15	4	0.05
5001	Adira Ramaswamy	1	1	5	2	0.05
5002	Catherine Bolton	1	1	5	2	0.05
5003	Christina Villa	1	1	5	2	0.05
5004	Christine Hauffer	1	1	5	2	0.05
5005	Daniela Scholtz	1	1	5	2	0.05
5006	Drishya Setty	1	1	5	2	0.05
5007	Jennifer Alvarez	1	1	5	2	0.05
5008	Jonathan Adams	1	1	5	2	0.05
5009	Kristen Edwards	1	1	5	2	0.05
5010	Laurent-Léon Morvan	1	1	5	2	0.05
5011	Lori Massey	1	1	5	2	0.05
5012	Lucie Tanguy	1	1	5	2	0.05
5013	Patrick Banks	1	1	5	2	0.05
5014	Philippe Carre de la Humbert	1	1	5	2	0.05
5015	Randy Stewart	1	1	5	2	0.05
5016	Ronald Ramirez	1	1	5	2	0.05
5017	Samar Dutta	1	1	5	2	0.05
5018	Sarah Patrick	1	1	5	2	0.05
5019	Subterranean Whispers	1	1	5	2	0.05
5020	William White	1	1	5	2	0.05
5021	Adam Smith	1	1	12	0	0.05
5022	Adrian Ross	1	1	12	0	0.05
5023	Alexander Rose	1	1	12	0	0.05
5024	Audrey Fritz	1	1	12	0	0.05
5025	Bartek Heleniak	1	1	12	0	0.05
5026	Christopher Fischer	1	1	12	0	0.05
5027	Craig Villanueva	1	1	12	0	0.05
5028	David Robinson	1	1	12	0	0.05
5029	Denny Scholl-Vogt	1	1	12	0	0.05
5030	Dominique Newton	1	1	12	0	0.05
5031	Dylan Miller	1	1	12	0	0.05
5032	Elaine Miller	1	1	12	0	0.05
5033	Felizitas Hermann	1	1	12	0	0.05
5034	Jerzy Majczyna	1	1	12	0	0.05
5035	Jonathan Gonzales	1	1	12	0	0.05
5036	Karen Rodriguez	1	1	12	0	0.05
5037	Mackenzie Garza	1	1	12	0	0.05
5038	Magret Staude	1	1	12	0	0.05
5039	Patricia Maynard	1	1	12	0	0.05
5040	Paul Hale	1	1	12	0	0.05
5041	Rebecca Bennett	1	1	12	0	0.05
5042	Regina Yang	1	1	12	0	0.05
5043	Rhonda Mendez	1	1	12	0	0.05
5044	Sara Chung	1	1	12	0	0.05
5045	Sonia Trochimiuk	1	1	12	0	0.05
5046	Summer Diaz	1	1	12	0	0.05
5047	Thomas Robinson	1	1	12	0	0.05
5048	Xiulan Qian	1	1	12	0	0.05
5049	Alexander Ramirez	1	1	8	1	0.05
5050	Amanda Boitani	1	1	8	1	0.05
5051	Amanda Lee	1	1	8	1	0.05
5052	Amy Frazier	1	1	8	1	0.05
5053	Anastazja Siedlik	1	1	8	1	0.05
5054	Andrew Gordon	1	1	8	1	0.05
5055	Anna Coleman	1	1	8	1	0.05
5056	Annetta Dallapé	1	1	8	1	0.05
5057	Anthony Navarro	1	1	8	1	0.05
5058	Baptist Adolph	1	1	8	1	0.05
5059	Beverly Lloyd	1	1	8	1	0.05
5060	Bobby Ruiz	1	1	8	1	0.05
5061	Bradley Mendez	1	1	8	1	0.05
5062	Breanna Neal	1	1	8	1	0.05
5063	Calcedonio Abbagnale	1	1	8	1	0.05
5064	Carrie Lang	1	1	8	1	0.05
5065	Carrie Pace	1	1	8	1	0.05
5066	Cesar Mcpherson	1	1	8	1	0.05
5067	Chao Feng	1	1	8	1	0.05
5068	Crystal Caldwell	1	1	8	1	0.05
5069	Dana Griffin	1	1	8	1	0.05
5070	Darlene Chase	1	1	8	1	0.05
5071	David Higgins	1	1	8	1	0.05
5072	Dean Mullins	1	1	8	1	0.05
5073	Deborah Odonnell	1	1	8	1	0.05
5074	Diana Johnson	1	1	8	1	0.05
5075	Eduardo Gonzalez	1	1	8	1	0.05
5076	Elizabeth Evans	1	1	8	1	0.05
5077	Elizabeth Martin	1	1	8	1	0.05
5078	Federico Offredi	1	1	8	1	0.05
5079	Guido Sbarbaro	1	1	8	1	0.05
5080	Guiying Xiong	1	1	8	1	0.05
5081	Haley Williams	1	1	8	1	0.05
5082	Heer Balan	1	1	8	1	0.05
5083	Heinz-Günter Kallert	1	1	8	1	0.05
5084	Ishaan Mahal	1	1	8	1	0.05
5085	James Gibson	1	1	8	1	0.05
5086	Jamie Davis	1	1	8	1	0.05
5087	Jie Kong	1	1	8	1	0.05
5088	Joel Collins	1	1	8	1	0.05
5089	Joseph Brooks	1	1	8	1	0.05
5090	Joyce Jackson	1	1	8	1	0.05
5091	Jun Ma	1	1	8	1	0.05
5092	Justin Morse	1	1	8	1	0.05
5093	Kajetan Ryszkiewicz	1	1	8	1	0.05
5094	Karen Garcia	1	1	8	1	0.05
5095	Katherine Watts	1	1	8	1	0.05
5096	Katrina Valentine	1	1	8	1	0.05
5097	Kristen Rice	1	1	8	1	0.05
5098	Kyle Daniel	1	1	8	1	0.05
5099	Laura Roberts	1	1	8	1	0.05
5100	Laura Sloan	1	1	8	1	0.05
5101	Leah Robinson	1	1	8	1	0.05
5102	Lei Xia	1	1	8	1	0.05
5103	Liwia Głuszko	1	1	8	1	0.05
5104	Lorenzo Orengo	1	1	8	1	0.05
5105	Madison Lopez	1	1	8	1	0.05
5106	Maria Fletcher	1	1	8	1	0.05
5107	Maria-Luise Klemm	1	1	8	1	0.05
5108	Mary Gomez	1	1	8	1	0.05
5109	Matthew Caldwell	1	1	8	1	0.05
5110	Matthew Gibson	1	1	8	1	0.05
5111	Matthew Li	1	1	8	1	0.05
5112	Matthew Roberson	1	1	8	1	0.05
5113	Michael Ramirez	1	1	8	1	0.05
5114	Mieszko Dorna	1	1	8	1	0.05
5115	Nadja Gnatz	1	1	8	1	0.05
5116	Nino Carfagna-Totino	1	1	8	1	0.05
5117	Oskar Radzewicz	1	1	8	1	0.05
5118	Patrick Davenport	1	1	8	1	0.05
5119	Paul Burns	1	1	8	1	0.05
5120	Robert Lopez	1	1	8	1	0.05
5121	Roger Allen	1	1	8	1	0.05
5122	Ruy Villa Coloma	1	1	8	1	0.05
5123	Ryan Bailey	1	1	8	1	0.05
5124	Samantha Walters	1	1	8	1	0.05
5125	Sara Hołubowicz	1	1	8	1	0.05
5126	Sean Aguirre	1	1	8	1	0.05
5127	Tammy Washington	1	1	8	1	0.05
5128	Tao Xiao	1	1	8	1	0.05
5129	Terry Castillo	1	1	8	1	0.05
5130	Thomas Stephens	1	1	8	1	0.05
5131	Tiffany Mckee	1	1	8	1	0.05
5132	Todd Reed	1	1	8	1	0.05
5133	Valerie Stafford	1	1	8	1	0.05
5134	Wendy Flores	1	1	8	1	0.05
5135	Wiktor Kazimierczyk	1	1	8	1	0.05
5136	William Hawkins	1	1	8	1	0.05
5137	William Lynch	1	1	8	1	0.05
5138	Xia He	1	1	8	1	0.05
5139	Yong Li	1	1	8	1	0.05
5140	Yuvaan Chopra	1	1	8	1	0.05
5141	Amy Ochoa	1	1	4	2	0.05
5142	Calvin Kemp	1	1	4	2	0.05
5143	Erica Padilla	1	1	4	2	0.05
5144	Jason Lopez	1	1	4	2	0.05
5145	Juanita Galván	1	1	4	2	0.05
5146	Julia Smith	1	1	4	2	0.05
5147	Khushi Upadhyay	1	1	4	2	0.05
5148	Kumiko Fujita	1	1	4	2	0.05
5149	Maaya Tanaka	1	1	4	2	0.05
5150	Marcel Kutek	1	1	4	2	0.05
5151	Matthew Garcia	1	1	4	2	0.05
5152	Patrick Carney	1	1	4	2	0.05
5153	Paula Palmer	1	1	4	2	0.05
5154	William Rojas	1	1	4	2	0.05
5155	Yuta Sato	1	1	4	2	0.05
5156	Alex Mathis	1	1	11	0	0.05
5157	Allen Smith	1	1	11	0	0.05
5158	Amy Ramos	1	1	11	0	0.05
5159	Amy Wood	1	1	11	0	0.05
5160	Brenda Day	1	1	11	0	0.05
5161	Chelsea Young	1	1	11	0	0.05
5162	Christopher Nelson	1	1	11	0	0.05
5163	Dana Patterson	1	1	11	0	0.05
5164	Edward Dixon	1	1	11	0	0.05
5165	Edward Hamilton	1	1	11	0	0.05
5166	Ellen Bradley	1	1	11	0	0.05
5167	Garrett Dixon	1	1	11	0	0.05
5168	Jasmine Nichols	1	1	11	0	0.05
5169	Jason Stevens	1	1	11	0	0.05
5170	Jessica Brown	1	1	11	0	0.05
5171	Jose Morse	1	1	11	0	0.05
5172	Joshua Mckenzie	1	1	11	0	0.05
5173	Juan Tian	1	1	11	0	0.05
5174	Kelly Kim	1	1	11	0	0.05
5175	Kristen Carlson	1	1	11	0	0.05
5176	Leah Thomas	1	1	11	0	0.05
5177	Lisa Wright	1	1	11	0	0.05
5178	Luis Green	1	1	11	0	0.05
5179	Megan Diaz	1	1	11	0	0.05
5180	Nicole Hodges	1	1	11	0	0.05
5181	Ryan Carter	1	1	11	0	0.05
5182	Stephen Davis	1	1	11	0	0.05
5183	Timothy Watson	1	1	11	0	0.05
5184	Willie Johnson	1	1	11	0	0.05
5185	Yong Fang	1	1	11	0	0.05
5186	Alexander Rivera	1	1	7	1	0.05
5187	Alfredo Roth	1	1	7	1	0.05
5188	Allison Duran	1	1	7	1	0.05
5189	Amanda Ramos	1	1	7	1	0.05
5190	Amy Coleman	1	1	7	1	0.05
5191	Amy Gutierrez	1	1	7	1	0.05
5192	Andrea Obrien	1	1	7	1	0.05
5193	Ann Petersen	1	1	7	1	0.05
5194	Antonia Farinelli	1	1	7	1	0.05
5195	Antonia Mülichen	1	1	7	1	0.05
5196	Brandon Mccormick	1	1	7	1	0.05
5197	Brandon Payne	1	1	7	1	0.05
5198	Brian Perez	1	1	7	1	0.05
5199	Brian Ramos	1	1	7	1	0.05
5200	Bryan Sandoval	1	1	7	1	0.05
5201	Carl Rush	1	1	7	1	0.05
5202	Chao Huang	1	1	7	1	0.05
5203	Christian Rojas	1	1	7	1	0.05
5204	Ciro Campano	1	1	7	1	0.05
5205	Claudius Geißler	1	1	7	1	0.05
5206	Cynthia Robbins	1	1	7	1	0.05
5207	Dana Martin	1	1	7	1	0.05
5208	Daniel Aubry	1	1	7	1	0.05
5209	Daryl Parsons	1	1	7	1	0.05
5210	David Bowen	1	1	7	1	0.05
5211	Deborah Davis	1	1	7	1	0.05
5212	Debra Lam	1	1	7	1	0.05
5213	Derek Carpenter	1	1	7	1	0.05
5214	Dionigi Granatelli	1	1	7	1	0.05
5215	Donna Bennett	1	1	7	1	0.05
5216	Egon Wilmsen-Röhrdanz	1	1	7	1	0.05
5217	Elena Germano	1	1	7	1	0.05
5218	Elisa Zoppetti-Venturi	1	1	7	1	0.05
5219	Elizabeth Newton PhD	1	1	7	1	0.05
5220	Eugene Turner	1	1	7	1	0.05
5221	Florence Hermighausen	1	1	7	1	0.05
5222	Gabriel Seip	1	1	7	1	0.05
5223	Gang Gong	1	1	7	1	0.05
5224	Gang Song	1	1	7	1	0.05
5225	Greco Guarato	1	1	7	1	0.05
5226	Guido Conti	1	1	7	1	0.05
5227	Guiying Yu	1	1	7	1	0.05
5228	Hannes Ziegert	1	1	7	1	0.05
5229	Heidi Bradley	1	1	7	1	0.05
5230	Heidi Richardson	1	1	7	1	0.05
5231	Holly Riley	1	1	7	1	0.05
5232	Ignacy Henke	1	1	7	1	0.05
5233	Jacqueline Lawson	1	1	7	1	0.05
5234	James Dudley	1	1	7	1	0.05
5235	James Valentine	1	1	7	1	0.05
5236	Jamie Rodgers	1	1	7	1	0.05
5237	Jason Garcia	1	1	7	1	0.05
5238	Javi Fuente Benavente	1	1	7	1	0.05
5239	Jenny Dunn	1	1	7	1	0.05
5240	Jeremy Rodriguez	1	1	7	1	0.05
5241	Jerzy Ksel	1	1	7	1	0.05
5242	Jessica Bradley	1	1	7	1	0.05
5243	Jie Chang	1	1	7	1	0.05
5244	Jie Shi	1	1	7	1	0.05
5245	Jill Conley	1	1	7	1	0.05
5246	Joseph Alvarez	1	1	7	1	0.05
5247	Julie Mcgrath	1	1	7	1	0.05
5248	Julie Rowe	1	1	7	1	0.05
5249	Kari Dillon	1	1	7	1	0.05
5250	Kathy Phillips	1	1	7	1	0.05
5251	Kelsey Martinez	1	1	7	1	0.05
5252	Kenneth Moore	1	1	7	1	0.05
5253	Kent Chen	1	1	7	1	0.05
5254	Konstanty Drobek	1	1	7	1	0.05
5255	Li Xiang	1	1	7	1	0.05
5256	Lilla Giammusso	1	1	7	1	0.05
5257	Marcin Bernatek	1	1	7	1	0.05
5258	Marina Boucsein	1	1	7	1	0.05
5259	Marissa Evans	1	1	7	1	0.05
5260	Mariusz Burzec	1	1	7	1	0.05
5261	Mark Stein	1	1	7	1	0.05
5262	Matthew Crawford	1	1	7	1	0.05
5263	Matthew Williams	1	1	7	1	0.05
5264	Mattias Pohl	1	1	7	1	0.05
5265	Meagan Hendrix	1	1	7	1	0.05
5266	Megan Garcia	1	1	7	1	0.05
5267	Michael Buchanan	1	1	7	1	0.05
5268	Michael Frederick	1	1	7	1	0.05
5269	Mituru Matsumoto	1	1	7	1	0.05
5270	Na Lin	1	1	7	1	0.05
5271	Naoki Sato	1	1	7	1	0.05
5272	Nichole Moore	1	1	7	1	0.05
5273	Patricia Massey	1	1	7	1	0.05
5274	Paul Baum	1	1	7	1	0.05
5275	Phillip Martinez	1	1	7	1	0.05
5276	Ping Qiu	1	1	7	1	0.05
5277	Regina Davis	1	1	7	1	0.05
5278	Rika Maeda	1	1	7	1	0.05
5279	Robert Stewart	1	1	7	1	0.05
5280	Russell Bates	1	1	7	1	0.05
5281	Salvi Giorgetti	1	1	7	1	0.05
5282	Sarah Craig	1	1	7	1	0.05
5283	Sarah Harrison	1	1	7	1	0.05
5284	Satomi Inoue	1	1	7	1	0.05
5285	Sole Gagliano-Conte	1	1	7	1	0.05
5286	Stephanie Grant	1	1	7	1	0.05
5287	Suzanne Davis	1	1	7	1	0.05
5288	Tammy Griffin	1	1	7	1	0.05
5289	Taylor Pace	1	1	7	1	0.05
5290	Teresa Rivera	1	1	7	1	0.05
5291	Terrance Velazquez	1	1	7	1	0.05
5292	Theda Rosemann	1	1	7	1	0.05
5293	Thomas Miranda	1	1	7	1	0.05
5294	Théodore de la Chauvet	1	1	7	1	0.05
5295	Traci Thomas	1	1	7	1	0.05
5296	Wanda Smith	1	1	7	1	0.05
5297	Whitney Humphrey	1	1	7	1	0.05
5298	Whitney Torres	1	1	7	1	0.05
5299	William Porter	1	1	7	1	0.05
5300	Xia Peng	1	1	7	1	0.05
5301	Yan Yao	1	1	7	1	0.05
5302	Li Fang	1	0	13	4	0.05
5303	Antonia Malaparte-Leonardi	1	1	3	2	0.05
5304	Elizabeth Myers	1	1	3	2	0.05
5305	Erica Smith	1	1	3	2	0.05
5306	Nikol Hrubá	1	1	3	2	0.05
5307	Patrick Leleu	1	1	3	2	0.05
5308	Rosita Vollbrecht	1	1	3	2	0.05
5309	Rémy-Richard Devaux	1	1	3	2	0.05
5310	Brian Brewer	1	1	10	0	0.05
5311	Brittany Holmes	1	1	10	0	0.05
5312	Casey Farrell	1	1	10	0	0.05
5313	Isabell Wulff	1	1	10	0	0.05
5314	Jackson Monroe	1	1	10	0	0.05
5315	Loïs Knoers	1	1	10	0	0.05
5316	Megan Raymond	1	1	10	0	0.05
5317	Michael Bradford	1	1	10	0	0.05
5318	Randall Johnson	1	1	10	0	0.05
5319	Tao Qiu	1	1	10	0	0.05
5320	Abigaíl Tovar Sainz	1	1	6	1	0.04
5321	Adrian Miller	1	1	6	1	0.04
5322	Alexej Klotz	1	1	6	1	0.04
5323	Alfonso Albero Cervera	1	1	6	1	0.04
5324	Alfred Tschentscher	1	1	6	1	0.04
5325	Anastasio Jiménez Ferrer	1	1	6	1	0.04
5326	Annegret Nerger	1	1	6	1	0.04
5327	Ashley Brady	1	1	6	1	0.04
5328	Autumn Walker	1	1	6	1	0.04
5329	Barbara Fox	1	1	6	1	0.04
5330	Barry Wilkerson	1	1	6	1	0.04
5331	Bartek Tatarczyk	1	1	6	1	0.04
5332	Benoît Blanc	1	1	6	1	0.04
5333	Bianca Rice	1	1	6	1	0.04
5334	Brandon Barton	1	1	6	1	0.04
5335	Brett Larson	1	1	6	1	0.04
5336	Brian Brown	1	1	6	1	0.04
5337	Brian Gross	1	1	6	1	0.04
5338	Cassandra Washington	1	1	6	1	0.04
5339	Cayetana Bernal-Ocaña	1	1	6	1	0.04
5340	Charo Cañas Saez	1	1	6	1	0.04
5341	Cheryl Gibson	1	1	6	1	0.04
5342	Cheryl Walker	1	1	6	1	0.04
5343	Christopher Gutierrez	1	1	6	1	0.04
5344	Christopher Owens	1	1	6	1	0.04
5345	Christopher Whitaker	1	1	6	1	0.04
5346	Concepción Ripoll	1	1	6	1	0.04
5347	Cynthia Johns	1	1	6	1	0.04
5348	Damini Datta	1	1	6	1	0.04
5349	Debbie Fuller	1	1	6	1	0.04
5350	Dishani Goswami	1	1	6	1	0.04
5351	Divit Khanna	1	1	6	1	0.04
5352	Ehsaan Lall	1	1	6	1	0.04
5353	Elfriede Reuter	1	1	6	1	0.04
5354	Elsbeth Löwer	1	1	6	1	0.04
5355	Emelina Verdejo Otero	1	1	6	1	0.04
5356	Eric Shaw	1	1	6	1	0.04
5357	Evelína Kučerová	1	1	6	1	0.04
5358	Gang Meng	1	1	6	1	0.04
5359	Gerhart Wieloch	1	1	6	1	0.04
5360	Glenn Lewis	1	1	6	1	0.04
5361	Gloria Lewis	1	1	6	1	0.04
5362	Gregory Jones	1	1	6	1	0.04
5363	Hansh Hayer	1	1	6	1	0.04
5364	Herr Stephan Tschentscher	1	1	6	1	0.04
5365	Holly Stephens	1	1	6	1	0.04
5366	Ian Dorsey	1	1	6	1	0.04
5367	Inés del Valenciano	1	1	6	1	0.04
5368	Ivana Din	1	1	6	1	0.04
5369	Jacob Mcmahon	1	1	6	1	0.04
5370	Jan Hruška	1	1	6	1	0.04
5371	Jan Zadworny	1	1	6	1	0.04
5372	Jennifer Cox	1	1	6	1	0.04
5373	Jennifer Flores	1	1	6	1	0.04
5374	Jenny Rodriguez	1	1	6	1	0.04
5375	Jerry Ruiz	1	1	6	1	0.04
5376	Jessica Lucas	1	1	6	1	0.04
5377	Jessica Wright	1	1	6	1	0.04
5378	Jill Reynolds	1	1	6	1	0.04
5379	Jing Chen	1	1	6	1	0.04
5380	Jing Shao	1	1	6	1	0.04
5381	Jocelyn Davis	1	1	6	1	0.04
5382	John Brewer	1	1	6	1	0.04
5383	John Russo	1	1	6	1	0.04
5384	Johnathan Clark	1	1	6	1	0.04
5385	Joseph Ferguson	1	1	6	1	0.04
5386	Julita Łuszczak	1	1	6	1	0.04
5387	Karol Indyk	1	1	6	1	0.04
5388	Katherine Preston	1	1	6	1	0.04
5389	Katie Anderson	1	1	6	1	0.04
5390	Kelly Gilbert	1	1	6	1	0.04
5391	Kelly Reyes	1	1	6	1	0.04
5392	Kelly Williams	1	1	6	1	0.04
5393	Kim Preston	1	1	6	1	0.04
5394	Kimberly Cain	1	1	6	1	0.04
5395	Kornel Troka	1	1	6	1	0.04
5396	Lagan Bhalla	1	1	6	1	0.04
5397	Leif Bien	1	1	6	1	0.04
5398	Leslie Flynn	1	1	6	1	0.04
5399	Lisa Brooks	1	1	6	1	0.04
5400	Lorenz Förster-Barth	1	1	6	1	0.04
5401	Lukáš Strnad	1	1	6	1	0.04
5402	Malwina Kaźmierczyk	1	1	6	1	0.04
5403	Mannat Chopra	1	1	6	1	0.04
5404	Margaretha Sager	1	1	6	1	0.04
5405	Mary Powers	1	1	6	1	0.04
5406	Meredith Hamilton	1	1	6	1	0.04
5407	Michael Dickerson	1	1	6	1	0.04
5408	Michelle Eaton	1	1	6	1	0.04
5409	Miloslava Nováková	1	1	6	1	0.04
5410	Ming Gong	1	1	6	1	0.04
5411	Ming Lu	1	1	6	1	0.04
5412	Mitchell Flowers	1	1	6	1	0.04
5413	Mitchell Mcpherson	1	1	6	1	0.04
5414	Momoko Tanaka	1	1	6	1	0.04
5415	Nakul Samra	1	1	6	1	0.04
5416	Nanami Takahashi	1	1	6	1	0.04
5417	Nehmat Chaudhary	1	1	6	1	0.04
5418	Nicole Yoder	1	1	6	1	0.04
5419	Nitara Biswas	1	1	6	1	0.04
5420	Noah Braun	1	1	6	1	0.04
5421	Pari Chand	1	1	6	1	0.04
5422	Paul Harrell	1	1	6	1	0.04
5423	Ping Hu	1	1	6	1	0.04
5424	Ping Zheng	1	1	6	1	0.04
5425	Prerak Majumdar	1	1	6	1	0.04
5426	René Sans	1	1	6	1	0.04
5427	Richard Smith	1	1	6	1	0.04
5428	Rick Franklin	1	1	6	1	0.04
5429	Robert Fronia	1	1	6	1	0.04
5430	Robert Roberts	1	1	6	1	0.04
5431	Roman Švec	1	1	6	1	0.04
5432	Russell Barnes	1	1	6	1	0.04
5433	Sahil Tripathi	1	1	6	1	0.04
5434	Sarah Galloway	1	1	6	1	0.04
5435	Shari Willis	1	1	6	1	0.04
5436	Shelby Grimes	1	1	6	1	0.04
5437	Sophie Lebrun	1	1	6	1	0.04
5438	Stefan Zirme	1	1	6	1	0.04
5439	Steve Guzman	1	1	6	1	0.04
5440	Steven Schultz	1	1	6	1	0.04
5441	Szymon Pazik	1	1	6	1	0.04
5442	Tadeusz Gemza	1	1	6	1	0.04
5443	Terri Preston	1	1	6	1	0.04
5444	Theresa Mason	1	1	6	1	0.04
5445	Timothy Hansen	1	1	6	1	0.04
5446	Tracy Burton	1	1	6	1	0.04
5447	Tracy Ibarra	1	1	6	1	0.04
5448	Tracy Simpson	1	1	6	1	0.04
5449	Trevor Weiss	1	1	6	1	0.04
5450	Tyler Padilla	1	1	6	1	0.04
5451	Victor Spencer	1	1	6	1	0.04
5452	Victoria Johnson	1	1	6	1	0.04
5453	Viktor Švec	1	1	6	1	0.04
5454	Wigbert Scholl	1	1	6	1	0.04
5455	William Mcbride	1	1	6	1	0.04
5456	Xia Hou	1	1	6	1	0.04
5457	Yong Xie	1	1	6	1	0.04
5458	Yosuke Matsumoto	1	1	6	1	0.04
5459	Yui Watanabe	1	1	6	1	0.04
5460	Zara Anand	1	1	6	1	0.04
5461	Štěpánka Bártová	1	1	6	1	0.04
5462	Jeanne Perrin	1	1	2	2	0.04
5463	Marguerite Faure	1	1	2	2	0.04
5464	Alicia Kelly	1	1	9	0	0.04
5465	Brenda Stark	1	1	9	0	0.04
5466	Brittany Strickland	1	1	9	0	0.04
5467	Colleen Palmer	1	1	9	0	0.04
5468	Connie Estrada	1	1	9	0	0.04
5469	Danny Hill	1	1	9	0	0.04
5470	Denise Fisher	1	1	9	0	0.04
5471	Elizabeth Stone	1	1	9	0	0.04
5472	Frank Shaffer	1	1	9	0	0.04
5473	Gang Wu	1	1	9	0	0.04
5474	Guiying Wan	1	1	9	0	0.04
5475	Jacob Martin	1	1	9	0	0.04
5476	John Guzman II	1	1	9	0	0.04
5477	Jordan Thomas	1	1	9	0	0.04
5478	Joy Fletcher	1	1	9	0	0.04
5479	Karen Graham	1	1	9	0	0.04
5480	Katherine Bradley	1	1	9	0	0.04
5481	Kevin Rush	1	1	9	0	0.04
5482	Kimberly Collins	1	1	9	0	0.04
5483	Kimberly Jordan	1	1	9	0	0.04
5484	Kimberly Young	1	1	9	0	0.04
5485	Kristy Nelson	1	1	9	0	0.04
5486	Lara Horrocks	1	1	9	0	0.04
5487	Larry Jones	1	1	9	0	0.04
5488	Laura Rodriguez	1	1	9	0	0.04
5489	Lisa Nunez	1	1	9	0	0.04
5490	Lola Vermeulen	1	1	9	0	0.04
5491	Marc Andersen	1	1	9	0	0.04
5492	Mathew Schultz	1	1	9	0	0.04
5493	Matthew Hill	1	1	9	0	0.04
5494	Michelle Hensley	1	1	9	0	0.04
5495	Pamela Rollins	1	1	9	0	0.04
5496	Patricia Fisher	1	1	9	0	0.04
5497	Paul Lopez	1	1	9	0	0.04
5498	Peter Garcia	1	1	9	0	0.04
5499	Qiang Su	1	1	9	0	0.04
5500	Qiang Wen	1	1	9	0	0.04
5501	Robert Brown	1	1	9	0	0.04
5502	Rose Dean	1	1	9	0	0.04
5503	Samuel Ryan	1	1	9	0	0.04
5504	Sean Peck	1	1	9	0	0.04
5505	Sosimo Vila Bayona	1	1	9	0	0.04
5506	Travis Thompson	1	1	9	0	0.04
5507	Xiulan Xia	1	1	9	0	0.04
5508	Yong Xiong	1	1	9	0	0.04
5509	Adrianna Kurc	1	1	5	1	0.04
5510	Advika Ramakrishnan	1	1	5	1	0.04
5511	Alejandro Montenegro-Palomino	1	1	5	1	0.04
5512	Alexa Thomas	1	1	5	1	0.04
5513	Alonso del Benítez	1	1	5	1	0.04
5514	Amleto Fittipaldi	1	1	5	1	0.04
5515	Amy Mejia	1	1	5	1	0.04
5516	Anahi Sachdeva	1	1	5	1	0.04
5517	Anastazja Łuczyk	1	1	5	1	0.04
5518	Angela Hernandez	1	1	5	1	0.04
5519	Antonella Moschino	1	1	5	1	0.04
5520	Antonietta Chittolini	1	1	5	1	0.04
5521	April Welch	1	1	5	1	0.04
5522	Arsenio Berengario	1	1	5	1	0.04
5523	Aurélie de la Lucas	1	1	5	1	0.04
5524	Bethany Thompson	1	1	5	1	0.04
5525	Bianca Mentzel	1	1	5	1	0.04
5526	Birger Klemt	1	1	5	1	0.04
5527	Caleb Long	1	1	5	1	0.04
5528	Carlito Martí Jaume	1	1	5	1	0.04
5529	Carolyn Sharp	1	1	5	1	0.04
5530	Carsten Heinz	1	1	5	1	0.04
5531	Casey Scott	1	1	5	1	0.04
5532	Cassandra Zito	1	1	5	1	0.04
5533	Caterina Mercadante-Munari	1	1	5	1	0.04
5534	Cheryl White	1	1	5	1	0.04
5535	Chiyo Hashimoto	1	1	5	1	0.04
5536	Christina Martin	1	1	5	1	0.04
5537	Christine Olson	1	1	5	1	0.04
5538	Corey Stephens	1	1	5	1	0.04
5539	Craig Gonzalez	1	1	5	1	0.04
5540	Dagmara Pasztaleniec	1	1	5	1	0.04
5541	Daniel Harrington	1	1	5	1	0.04
5542	Danielle Ball	1	1	5	1	0.04
5543	Dawn Martinez	1	1	5	1	0.04
5544	Deanna Johnson	1	1	5	1	0.04
5545	Deanna Williams	1	1	5	1	0.04
5546	Debbie Hall	1	1	5	1	0.04
5547	Diana Lewis	1	1	5	1	0.04
5548	Ela Dara	1	1	5	1	0.04
5549	Elba Zaragoza-Doménech	1	1	5	1	0.04
5550	Eliana Marini	1	1	5	1	0.04
5551	Elizabeth Baker	1	1	5	1	0.04
5552	Elizabeth Brown	1	1	5	1	0.04
5553	Elizabeth Webb	1	1	5	1	0.04
5554	Ernestine Franke-Kade	1	1	5	1	0.04
5555	Eugenia Lindner	1	1	5	1	0.04
5556	Fang Zeng	1	1	5	1	0.04
5557	Gabriel Silva	1	1	5	1	0.04
5558	Garrett Salazar	1	1	5	1	0.04
5559	George Snyder	1	1	5	1	0.04
5560	Gesine Gehringer-Rogner	1	1	5	1	0.04
5561	Giampiero Lombroso	1	1	5	1	0.04
5562	Gianfranco Trentini	1	1	5	1	0.04
5563	Gianluca Barberini	1	1	5	1	0.04
5564	Gordana Schwital	1	1	5	1	0.04
5565	Gregory Huffman	1	1	5	1	0.04
5566	Griselda Renier-Mozart	1	1	5	1	0.04
5567	Hans-Willi Henschel	1	1	5	1	0.04
5568	Hansh Dyal	1	1	5	1	0.04
5569	Heinz-Georg Segebahn	1	1	5	1	0.04
5570	Henrike Pärtzelt	1	1	5	1	0.04
5571	Herminia Vazquez-Sainz	1	1	5	1	0.04
5572	Herr Detlef Geißler	1	1	5	1	0.04
5573	Herr Ulrich Gieß	1	1	5	1	0.04
5574	Hidde Huisman	1	1	5	1	0.04
5575	Indrajit Borra	1	1	5	1	0.04
5576	Isaac Joubert	1	1	5	1	0.04
5577	Itziar Mata Marqués	1	1	5	1	0.04
5578	Ivana Bains	1	1	5	1	0.04
5579	Iwo Olbrych	1	1	5	1	0.04
5580	Iwo Zbrzeźniak	1	1	5	1	0.04
5581	James Bauer	1	1	5	1	0.04
5582	James Munoz	1	1	5	1	0.04
5583	Janina Mazik	1	1	5	1	0.04
5584	Jared Banks	1	1	5	1	0.04
5585	Jasmine Thompson	1	1	5	1	0.04
5586	Jason Baker	1	1	5	1	0.04
5587	Jayan Chandran	1	1	5	1	0.04
5588	Jeremi Kwarciak	1	1	5	1	0.04
5589	Jeremy Hahn	1	1	5	1	0.04
5590	Jessica Parsons	1	1	5	1	0.04
5591	Jie Zeng	1	1	5	1	0.04
5592	Joann Smith	1	1	5	1	0.04
5593	Joel Miller	1	1	5	1	0.04
5594	Johannes de Vries	1	1	5	1	0.04
5595	Jolanta Schlosser	1	1	5	1	0.04
5596	Jonathan May	1	1	5	1	0.04
5597	Jorge Ross	1	1	5	1	0.04
5598	Jose Sanders	1	1	5	1	0.04
5599	Joseph Wheeler	1	1	5	1	0.04
5600	Julie Lane	1	1	5	1	0.04
5601	Józef Niewiara	1	1	5	1	0.04
5602	Kalina Fidor	1	1	5	1	0.04
5603	Katherine Wang	1	1	5	1	0.04
5604	Kathleen Russo	1	1	5	1	0.04
5605	Kelly Sanders	1	1	5	1	0.04
5606	Klara Betlej	1	1	5	1	0.04
5607	Kornelia Raczkiewicz	1	1	5	1	0.04
5608	Krista Rogge	1	1	5	1	0.04
5609	Kristina Taylor	1	1	5	1	0.04
5610	Leocadia Falcón Diez	1	1	5	1	0.04
5611	Lucia Corradi	1	1	5	1	0.04
5612	Manikya Badal	1	1	5	1	0.04
5613	Manon Voisin	1	1	5	1	0.04
5614	Marcelina Kosno	1	1	5	1	0.04
5615	Mateusz Perczak	1	1	5	1	0.04
5616	Matthew Hall	1	1	5	1	0.04
5617	Matthew Murphy	1	1	5	1	0.04
5618	Maurycy Bohdanowicz	1	1	5	1	0.04
5619	Mechthilde Kühnert	1	1	5	1	0.04
5620	Megan Mcclure	1	1	5	1	0.04
5621	Melissa Soto	1	1	5	1	0.04
5622	Michael Keller	1	1	5	1	0.04
5623	Michael Thompson	1	1	5	1	0.04
5624	Micheal Martin	1	1	5	1	0.04
5625	Michel Pichon	1	1	5	1	0.04
5626	Michelle Barnes	1	1	5	1	0.04
5627	Nacio Joel Corral Domingo	1	1	5	1	0.04
5628	Natasha Duncan	1	1	5	1	0.04
5629	Nathaniel Alvarado	1	1	5	1	0.04
5630	Nicola Napolitano	1	1	5	1	0.04
5631	Nicole Bowers	1	1	5	1	0.04
5632	Nicole Mccormick	1	1	5	1	0.04
5633	Nina Pareto	1	1	5	1	0.04
5634	Nishith Sachar	1	1	5	1	0.04
5635	Nitya Dass	1	1	5	1	0.04
5636	Nout Zijlmans	1	1	5	1	0.04
5637	Nynke Corstiaens	1	1	5	1	0.04
5638	Octavia Gilabert León	1	1	5	1	0.04
5639	Olivia Solomon	1	1	5	1	0.04
5640	Onkar Shankar	1	1	5	1	0.04
5641	Osamu Goto	1	1	5	1	0.04
5642	Pam Cunningham	1	1	5	1	0.04
5643	Paola Täsche	1	1	5	1	0.04
5644	Patrick Geisler	1	1	5	1	0.04
5645	Patrick Nguyen	1	1	5	1	0.04
5646	Perry Lopez	1	1	5	1	0.04
5647	Peter Collins	1	1	5	1	0.04
5648	Ping Long	1	1	5	1	0.04
5649	Piotr Wacław	1	1	5	1	0.04
5650	Pranay Mahajan	1	1	5	1	0.04
5651	Rafał Raba	1	1	5	1	0.04
5652	Rhonda Mcgee	1	1	5	1	0.04
5653	Rika Yamamoto	1	1	5	1	0.04
5654	Robert Cannon	1	1	5	1	0.04
5655	Robert King	1	1	5	1	0.04
5656	Robert Pugh	1	1	5	1	0.04
5657	Roksana Kurzyna	1	1	5	1	0.04
5658	Sara Jover Iglesias	1	1	5	1	0.04
5659	Sarah Ward	1	1	5	1	0.04
5660	Sean Rush	1	1	5	1	0.04
5661	Sebastiano Torlonia	1	1	5	1	0.04
5662	Segismundo Hoyos Múgica	1	1	5	1	0.04
5663	Sharon Bray	1	1	5	1	0.04
5664	Sharon Mcclain	1	1	5	1	0.04
5665	Shayak Chakraborty	1	1	5	1	0.04
5666	Sheila Cox	1	1	5	1	0.04
5667	Sheri Calhoun	1	1	5	1	0.04
5668	Sibylla Beyer	1	1	5	1	0.04
5669	Sibylla Heintze	1	1	5	1	0.04
5670	Susanne Pierre	1	1	5	1	0.04
5671	Tammy Wilcox	1	1	5	1	0.04
5672	Terry Clark	1	1	5	1	0.04
5673	Timothy Lopez	1	1	5	1	0.04
5674	Tiya Kota	1	1	5	1	0.04
5675	Umang Uppal	1	1	5	1	0.04
5676	William de la Valentin	1	1	5	1	0.04
5677	Xavier Pierre	1	1	5	1	0.04
5678	Yoko Nishimura	1	1	5	1	0.04
5679	Yong Huang	1	1	5	1	0.04
5680	Yong Ma	1	1	5	1	0.04
5681	Yong Yin	1	1	5	1	0.04
5682	Zacharie Morin	1	1	5	1	0.04
5683	Zara Choudhury	1	1	5	1	0.04
5684	Zeeshan Kuruvilla	1	1	5	1	0.04
5685	Aaron Medina	1	1	8	0	0.04
5686	Alfonso Bähr	1	1	8	0	0.04
5687	Andrew Ellison	1	1	8	0	0.04
5688	Annelise Berger	1	1	8	0	0.04
5689	Aurélie Traore de Grondin	1	1	8	0	0.04
5690	Billy Martinez	1	1	8	0	0.04
5691	Brian Mathis	1	1	8	0	0.04
5692	Brian Richard	1	1	8	0	0.04
5693	Candice Avila	1	1	8	0	0.04
5694	Carl Brown	1	1	8	0	0.04
5695	Carrie Smith	1	1	8	0	0.04
5696	Charles Thomas	1	1	8	0	0.04
5697	Curtis Cooper	1	1	8	0	0.04
5698	Deanna Green	1	1	8	0	0.04
5699	Douglas Gray	1	1	8	0	0.04
5700	Drew Green	1	1	8	0	0.04
5701	Edward Gutierrez	1	1	8	0	0.04
5702	Gang Qiu	1	1	8	0	0.04
5703	Gregory Morgan	1	1	8	0	0.04
5704	Gudula Mitschke-Reuter	1	1	8	0	0.04
5705	Henry Ortega	1	1	8	0	0.04
5706	Holly Garcia	1	1	8	0	0.04
5707	Hubert Oderwald	1	1	8	0	0.04
5708	Hélène Lévêque	1	1	8	0	0.04
5709	Isaiah Gonzales	1	1	8	0	0.04
5710	Jason Lewis	1	1	8	0	0.04
5711	Jennifer Acosta	1	1	8	0	0.04
5712	Jeremy Watkins	1	1	8	0	0.04
5713	John Brooks	1	1	8	0	0.04
5714	Joshua Holmes	1	1	8	0	0.04
5715	Justin Carroll	1	1	8	0	0.04
5716	Karen Munoz	1	1	8	0	0.04
5717	Katie Smith	1	1	8	0	0.04
5718	Kimberly Foster	1	1	8	0	0.04
5719	Kimberly Kim	1	1	8	0	0.04
5720	Kristin Thomas	1	1	8	0	0.04
5721	Laura Howard	1	1	8	0	0.04
5722	Laura Johnson	1	1	8	0	0.04
5723	Lilo Christoph	1	1	8	0	0.04
5724	Lucas Jose Manuel Arnaiz Pinedo	1	1	8	0	0.04
5725	Maggie-Margot Vallée	1	1	8	0	0.04
5726	Mary Turner	1	1	8	0	0.04
5727	Megan Fox	1	1	8	0	0.04
5728	Michael Gibbs	1	1	8	0	0.04
5729	Miguel Shelton	1	1	8	0	0.04
5730	Nicole Anderson	1	1	8	0	0.04
5731	Olivier Munoz	1	1	8	0	0.04
5732	Paul Williams	1	1	8	0	0.04
5733	Phillip Stone	1	1	8	0	0.04
5734	Qiang Jin	1	1	8	0	0.04
5735	Rachel Hogan	1	1	8	0	0.04
5736	Ramona Petruzzi	1	1	8	0	0.04
5737	Raven Bowman	1	1	8	0	0.04
5738	Rebecca Ramirez	1	1	8	0	0.04
5739	Sara Estefanía Acedo Caro	1	1	8	0	0.04
5740	Sean Walters	1	1	8	0	0.04
5741	Steven Harris	1	1	8	0	0.04
5742	Tara Ortiz	1	1	8	0	0.04
5743	Timothy Beck	1	1	8	0	0.04
5744	Tracy Camacho	1	1	8	0	0.04
5745	William Peck	1	1	8	0	0.04
5746	Xiulan Kong	1	1	8	0	0.04
5747	Xiuying Gao	1	1	8	0	0.04
5748	Leo Hamilton	2	0	5	1	0.04
5749	Noah Bennett	2	0	5	1	0.04
5750	Aaron Martinez	1	1	4	1	0.04
5751	Adriana Trobbiani	1	1	4	1	0.04
5752	Advik Singh	1	1	4	1	0.04
5753	Akira Kobayashi	1	1	4	1	0.04
5754	Alicia Smith	1	1	4	1	0.04
5755	Alison Armstrong	1	1	4	1	0.04
5756	Allison Harris	1	1	4	1	0.04
5757	Andrew Barnes	1	1	4	1	0.04
5758	Angela Greene	1	1	4	1	0.04
5759	Ann Anderson	1	1	4	1	0.04
5760	Anouk Rivière	1	1	4	1	0.04
5761	Anthony Erickson	1	1	4	1	0.04
5762	April Crosby	1	1	4	1	0.04
5763	April Ortiz	1	1	4	1	0.04
5764	Ben le Gallen	1	1	4	1	0.04
5765	Benjamin Rogers	1	1	4	1	0.04
5766	Blake Melton	1	1	4	1	0.04
5767	Brandon Trujillo	1	1	4	1	0.04
5768	Brandon Williams	1	1	4	1	0.04
5769	Dale Stewart	1	1	4	1	0.04
5770	David Smith	1	1	4	1	0.04
5771	Deborah White	1	1	4	1	0.04
5772	Donna Anderson	1	1	4	1	0.04
5773	Douglas Stout	1	1	4	1	0.04
5774	Edward Ellis	1	1	4	1	0.04
5775	Elizabeth Phillips	1	1	4	1	0.04
5776	Fang Ye	1	1	4	1	0.04
5777	Farhan Bansal	1	1	4	1	0.04
5778	Franziska Lindau	1	1	4	1	0.04
5779	Gerold Sontag	1	1	4	1	0.04
5780	Graziella Marenzio-Scialpi	1	1	4	1	0.04
5781	Guiying Wen	1	1	4	1	0.04
5782	Heidelore Meister	1	1	4	1	0.04
5783	Herr Alexander Eckbauer	1	1	4	1	0.04
5784	Holly Lucero	1	1	4	1	0.04
5785	Jacqueline Hörle	1	1	4	1	0.04
5786	James Copeland	1	1	4	1	0.04
5787	Jessica Bernard	1	1	4	1	0.04
5788	Joan Brown	1	1	4	1	0.04
5789	Jocelyn Hall	1	1	4	1	0.04
5790	John Carlson	1	1	4	1	0.04
5791	Joseph Smith	1	1	4	1	0.04
5792	Joyce Norris	1	1	4	1	0.04
5793	Kashvi Dutt	1	1	4	1	0.04
5794	Katrina Guzman	1	1	4	1	0.04
5795	Kayla Grimes	1	1	4	1	0.04
5796	Kimaya Aggarwal	1	1	4	1	0.04
5797	Krish Thaker	1	1	4	1	0.04
5798	Kyle Johnson	1	1	4	1	0.04
5799	Leonie Kabus	1	1	4	1	0.04
5800	Leslie Page	1	1	4	1	0.04
5801	Lindsey Jackson	1	1	4	1	0.04
5802	Lucas Aguilar	1	1	4	1	0.04
5803	Maik Eckbauer	1	1	4	1	0.04
5804	Maria Priuli	1	1	4	1	0.04
5805	Marianna Drózd	1	1	4	1	0.04
5806	Mariola Häring	1	1	4	1	0.04
5807	Mark Schmidt	1	1	4	1	0.04
5808	Martin Frederick	1	1	4	1	0.04
5809	Mary Freeman	1	1	4	1	0.04
5810	Mary Gutierrez	1	1	4	1	0.04
5811	Matthew James	1	1	4	1	0.04
5812	Micha Weinhage	1	1	4	1	0.04
5813	Michael Wise	1	1	4	1	0.04
5814	Michael Wright	1	1	4	1	0.04
5815	Michelle Caldwell	1	1	4	1	0.04
5816	Miki Maeda	1	1	4	1	0.04
5817	Nelly Austermühle	1	1	4	1	0.04
5818	Nisa van de Biezenbos	1	1	4	1	0.04
5819	Nomadic Resonance	1	1	4	1	0.04
5820	Olivie Sauvage	1	1	4	1	0.04
5821	Olivier Antoine	1	1	4	1	0.04
5822	Pamela Griffin	1	1	4	1	0.04
5823	Paola Scheuermann	1	1	4	1	0.04
5824	Patrick Bertin	1	1	4	1	0.04
5825	Patrick Lindsey	1	1	4	1	0.04
5826	Pihu Ahuja	1	1	4	1	0.04
5827	Prisha Varkey	1	1	4	1	0.04
5828	Rebecca Austin	1	1	4	1	0.04
5829	Rebecca Webb	1	1	4	1	0.04
5830	Rhea Bajwa	1	1	4	1	0.04
5831	Rhonda Knox	1	1	4	1	0.04
5832	Riaan Balan	1	1	4	1	0.04
5833	Robert Jennings	1	1	4	1	0.04
5834	Rodney Moss	1	1	4	1	0.04
5835	Russell Ruiz	1	1	4	1	0.04
5836	Samar Sarma	1	1	4	1	0.04
5837	Sarah Hayes	1	1	4	1	0.04
5838	Scott Villa	1	1	4	1	0.04
5839	Shalv Dass	1	1	4	1	0.04
5840	Sophie Broeckx	1	1	4	1	0.04
5841	Théophile de Carlier	1	1	4	1	0.04
5842	Valeska Heinrich	1	1	4	1	0.04
5843	Vanessa King	1	1	4	1	0.04
5844	Virginia White	1	1	4	1	0.04
5845	William Burton	1	1	4	1	0.04
5846	Xiulan Han	1	1	4	1	0.04
5847	Yara Robbrechts Bruijne	1	1	4	1	0.04
5848	Zaina Zacharia	1	1	4	1	0.04
5849	Christine Robbins	1	0	17	2	0.04
5850	Dennis Hill PhD	1	0	17	2	0.04
5851	Amy Gonzalez	1	1	7	0	0.04
5852	Annunziata Disdero	1	1	7	0	0.04
5853	Brian Nguyen	1	1	7	0	0.04
5854	Bryan Matthews	1	1	7	0	0.04
5855	Bryan Shaw	1	1	7	0	0.04
5856	Camilla Zaccagnini-Bertoni	1	1	7	0	0.04
5857	Carlos Cannon	1	1	7	0	0.04
5858	Carrie Perry	1	1	7	0	0.04
5859	Catherine Black	1	1	7	0	0.04
5860	Christina Robinson	1	1	7	0	0.04
5861	Christopher Romero	1	1	7	0	0.04
5862	Diana Trujillo	1	1	7	0	0.04
5863	Emilio Farnese	1	1	7	0	0.04
5864	Fortunata Zeffirelli	1	1	7	0	0.04
5865	Francisco Campbell	1	1	7	0	0.04
5866	Friedbert Stey	1	1	7	0	0.04
5867	Gary Wallace	1	1	7	0	0.04
5868	Gina Cooper	1	1	7	0	0.04
5869	Giustino Carocci	1	1	7	0	0.04
5870	Guiying Li	1	1	7	0	0.04
5871	Hannah Payne	1	1	7	0	0.04
5872	Hannah Richards	1	1	7	0	0.04
5873	Hans-H. Jacobi Jäckel	1	1	7	0	0.04
5874	Harold Gutierrez	1	1	7	0	0.04
5875	Isabella Cook	1	1	7	0	0.04
5876	Jacopo Vianello	1	1	7	0	0.04
5877	Jesus Rivas	1	1	7	0	0.04
5878	Jie Yu	1	1	7	0	0.04
5879	Jill Goodman	1	1	7	0	0.04
5880	Job Mahieu	1	1	7	0	0.04
5881	Jonathan Horne	1	1	7	0	0.04
5882	Joseph Maxwell	1	1	7	0	0.04
5883	Juan Cui	1	1	7	0	0.04
5884	Juan Xie	1	1	7	0	0.04
5885	Kayla Taylor	1	1	7	0	0.04
5886	Kim Gonzalez	1	1	7	0	0.04
5887	Lara Scaramucci-Santi	1	1	7	0	0.04
5888	Laura Ward	1	1	7	0	0.04
5889	Laura Wolf	1	1	7	0	0.04
5890	Linda Ward	1	1	7	0	0.04
5891	Lindsay Marsh	1	1	7	0	0.04
5892	Marc Drake	1	1	7	0	0.04
5893	Maria Merritt	1	1	7	0	0.04
5894	Mary Griffin	1	1	7	0	0.04
5895	Matthew Gardner	1	1	7	0	0.04
5896	Mattia Pepe	1	1	7	0	0.04
5897	Michael Gordon	1	1	7	0	0.04
5898	Monica Dean	1	1	7	0	0.04
5899	Morgan Brown	1	1	7	0	0.04
5900	Na Xiong	1	1	7	0	0.04
5901	Na Xue	1	1	7	0	0.04
5902	Phillip Jones	1	1	7	0	0.04
5903	Raymond Ponce	1	1	7	0	0.04
5904	Rebecca Ruiz	1	1	7	0	0.04
5905	Robert Anderson	1	1	7	0	0.04
5906	Ryan Blevins	1	1	7	0	0.04
5907	Samantha Snow	1	1	7	0	0.04
5908	Sarah Wilson	1	1	7	0	0.04
5909	Shannon Bennett	1	1	7	0	0.04
5910	Shannon Romero	1	1	7	0	0.04
5911	Sharon Grant	1	1	7	0	0.04
5912	Sharon Parrish	1	1	7	0	0.04
5913	Steven Cordova	1	1	7	0	0.04
5914	Steven Keller	1	1	7	0	0.04
5915	Susan Meadows	1	1	7	0	0.04
5916	Tao Fu	1	1	7	0	0.04
5917	Tao Xia	1	1	7	0	0.04
5918	Thomas Gray	1	1	7	0	0.04
5919	Valerie Matthews	1	1	7	0	0.04
5920	Valerie Navarro	1	1	7	0	0.04
5921	Xiuying Jiang	1	1	7	0	0.04
5922	Yang Sun	1	1	7	0	0.04
5923	Adam Harmon	1	1	3	1	0.04
5924	Angela Wolfe	1	1	3	1	0.04
5925	Annette Collins	1	1	3	1	0.04
5926	Ashley Castillo	1	1	3	1	0.04
5927	Bianka Tabak	1	1	3	1	0.04
5928	Caitlin Allen	1	1	3	1	0.04
5929	Calogero Zamorani	1	1	3	1	0.04
5930	Chelsea Mclean	1	1	3	1	0.04
5931	Chloe Little	1	1	3	1	0.04
5932	Christie Woods	1	1	3	1	0.04
5933	Christopher Humphrey	1	1	3	1	0.04
5934	Costantino Ramazzotti	1	1	3	1	0.04
5935	Dagmara Smorąg	1	1	3	1	0.04
5936	Elizabeth Hill	1	1	3	1	0.04
5937	Elvira Puente Alba	1	1	3	1	0.04
5938	Emily Campos	1	1	3	1	0.04
5939	Enzo Morlacchi-Grassi	1	1	3	1	0.04
5940	Fernanda Carocci	1	1	3	1	0.04
5941	Gabrielle-Susan Bousquet	1	1	3	1	0.04
5942	Gwendolyn Morgan	1	1	3	1	0.04
5943	Haley Lawson	1	1	3	1	0.04
5944	Haley Silva	1	1	3	1	0.04
5945	Henriette Pierre	1	1	3	1	0.04
5946	James Medina	1	1	3	1	0.04
5947	Janet Parks	1	1	3	1	0.04
5948	Jason Owen	1	1	3	1	0.04
5949	Jennifer Reese	1	1	3	1	0.04
5950	Jeremy Martinez	1	1	3	1	0.04
5951	Jillian Crane	1	1	3	1	0.04
5952	John Holt	1	1	3	1	0.04
5953	Jon Harris	1	1	3	1	0.04
5954	Jonathan Fisher	1	1	3	1	0.04
5955	Juliusz Kominiak	1	1	3	1	0.04
5956	Kenneth Maldonado	1	1	3	1	0.04
5957	Kevin Wells	1	1	3	1	0.04
5958	Kimberly Fernandez	1	1	3	1	0.04
5959	Klaus-Werner Gierschner	1	1	3	1	0.04
5960	Lara Filippelli	1	1	3	1	0.04
5961	Lawrence Larson	1	1	3	1	0.04
5962	Leslie Hinton	1	1	3	1	0.04
5963	Linda Burns	1	1	3	1	0.04
5964	Lindsay Browning	1	1	3	1	0.04
5965	Lindsey Broeshart	1	1	3	1	0.04
5966	Ludovico Sgarbi-Interminei	1	1	3	1	0.04
5967	Marcella Dobes	1	1	3	1	0.04
5968	Margherita Montanari	1	1	3	1	0.04
5969	Mariusz Segebahn	1	1	3	1	0.04
5970	Martin Jones PhD	1	1	3	1	0.04
5971	Matthew Best	1	1	3	1	0.04
5972	Maurizio Curatoli	1	1	3	1	0.04
5973	Megan Humphrey	1	1	3	1	0.04
5974	Michael Wilson	1	1	3	1	0.04
5975	Miss Kathleen Price	1	1	3	1	0.04
5976	Natalija Täsche	1	1	3	1	0.04
5977	Nathalie Striebitz	1	1	3	1	0.04
5978	Nicole Newman	1	1	3	1	0.04
5979	Noah Shaw	1	1	3	1	0.04
5980	Olaf Dymarczyk	1	1	3	1	0.04
5981	Orlando Garzoni	1	1	3	1	0.04
5982	Patrick Walsh	1	1	3	1	0.04
5983	Paul-Robert Bodin	1	1	3	1	0.04
5984	Pedro Mitschke	1	1	3	1	0.04
5985	Pieter van Dijk	1	1	3	1	0.04
5986	Rachel Hall	1	1	3	1	0.04
5987	Robert Stevenson	1	1	3	1	0.04
5988	Roger Walsh	1	1	3	1	0.04
5989	Rosario Alsina Saez	1	1	3	1	0.04
5990	Samantha Campbell	1	1	3	1	0.04
5991	Samuel Graham	1	1	3	1	0.04
5992	Sara Roman	1	1	3	1	0.04
5993	Sean Blackburn	1	1	3	1	0.04
5994	Shannon Thompson	1	1	3	1	0.04
5995	Sheila Acosta	1	1	3	1	0.04
5996	Shelby Hunt	1	1	3	1	0.04
5997	Sheri Hernandez	1	1	3	1	0.04
5998	Stephanie Bird	1	1	3	1	0.04
5999	Tammy Craig	1	1	3	1	0.04
6000	Thomas Clark	1	1	3	1	0.04
6001	Timothy Randolph	1	1	3	1	0.04
6002	Tracy Williamson	1	1	3	1	0.04
6003	Wesley Cain	1	1	3	1	0.04
6004	Wieslaw Heydrich	1	1	3	1	0.04
6005	William Blair	1	1	3	1	0.04
6006	Yong Xiang	1	1	3	1	0.04
6007	Zachary Rojas	1	1	3	1	0.04
6008	Aaron Horton	1	1	6	0	0.04
6009	Aarush Shukla	1	1	6	0	0.04
6010	Adam Hinton	1	1	6	0	0.04
6011	Agnolo Zaccardo-Leonetti	1	1	6	0	0.04
6012	Alberto Durante-Depero	1	1	6	0	0.04
6013	Alyssa Franklin	1	1	6	0	0.04
6014	Amy Conway	1	1	6	0	0.04
6015	Amy Thomas	1	1	6	0	0.04
6016	Anay Vohra	1	1	6	0	0.04
6017	Anja Niemeier	1	1	6	0	0.04
6018	Anthony Lane	1	1	6	0	0.04
6019	Aradhya Maharaj	1	1	6	0	0.04
6020	Armida Luque Mascaró	1	1	6	0	0.04
6021	Arnaldo Borzomì	1	1	6	0	0.04
6022	Ashley Logan	1	1	6	0	0.04
6023	Ashley Osborne	1	1	6	0	0.04
6024	Aurora Alboni	1	1	6	0	0.04
6025	Brandon Cohen	1	1	6	0	0.04
6026	Brandon Morales	1	1	6	0	0.04
6027	Brian Jones	1	1	6	0	0.04
6028	Briana Miller	1	1	6	0	0.04
6029	Calvin Cooper	1	1	6	0	0.04
6030	Carl Rodriguez	1	1	6	0	0.04
6031	Carmen Higgins	1	1	6	0	0.04
6032	Casey Wright	1	1	6	0	0.04
6033	Cemal Dowerg	1	1	6	0	0.04
6034	Chao Du	1	1	6	0	0.04
6035	Chao Ma	1	1	6	0	0.04
6036	Charles Marshall	1	1	6	0	0.04
6037	Charles Snyder	1	1	6	0	0.04
6038	Christina Soto	1	1	6	0	0.04
6039	Christopher Flores	1	1	6	0	0.04
6040	Christopher Jones	1	1	6	0	0.04
6041	Christopher Lowery	1	1	6	0	0.04
6042	Christopher Pham	1	1	6	0	0.04
6043	Concetta Davids	1	1	6	0	0.04
6044	Coriolano Riccati	1	1	6	0	0.04
6045	Craig Lewis	1	1	6	0	0.04
6046	Cristian Gross	1	1	6	0	0.04
6047	Cynthia Henson	1	1	6	0	0.04
6048	Danny Brooks	1	1	6	0	0.04
6049	David Cannon	1	1	6	0	0.04
6050	David Mayo	1	1	6	0	0.04
6051	Dawn Allen	1	1	6	0	0.04
6052	Dennis Bass	1	1	6	0	0.04
6053	Devin Wallace	1	1	6	0	0.04
6054	Diana White	1	1	6	0	0.04
6055	Diane Brooks	1	1	6	0	0.04
6056	Diane Segebahn	1	1	6	0	0.04
6057	Domenico Nette	1	1	6	0	0.04
6058	Doris Becker	1	1	6	0	0.04
6059	Ebony Acevedo	1	1	6	0	0.04
6060	Eddie Taylor	1	1	6	0	0.04
6061	Edward Parker	1	1	6	0	0.04
6062	Edwin Crosby	1	1	6	0	0.04
6063	Ela Batra	1	1	6	0	0.04
6064	Elizabeth Davis	1	1	6	0	0.04
6065	Elizabeth Spence	1	1	6	0	0.04
6066	Emily Gonzalez	1	1	6	0	0.04
6067	Eva Morosini-Procacci	1	1	6	0	0.04
6068	Eva-Maria Killer	1	1	6	0	0.04
6069	Fang Cheng	1	1	6	0	0.04
6070	Farhan Bava	1	1	6	0	0.04
6071	Franco Weitzel	1	1	6	0	0.04
6072	Gang Fang	1	1	6	0	0.04
6073	Gregory Rhodes	1	1	6	0	0.04
6074	Guiying Cheng	1	1	6	0	0.04
6075	Guiying Wang	1	1	6	0	0.04
6076	Gunhild Oderwald	1	1	6	0	0.04
6077	Hanako Suzuki	1	1	6	0	0.04
6078	Hansjoachim Gieß	1	1	6	0	0.04
6079	Heather Pollard	1	1	6	0	0.04
6080	Hector Smith	1	1	6	0	0.04
6081	Heidi Henry	1	1	6	0	0.04
6082	Heidi Mills	1	1	6	0	0.04
6083	Henriette Duval	1	1	6	0	0.04
6084	Herr Karl-Josef Roskoth	1	1	6	0	0.04
6085	Imran van Maasgouw-Schroeff	1	1	6	0	0.04
6086	Isis van Tours-Kathagen	1	1	6	0	0.04
6087	Jack Hawkins	1	1	6	0	0.04
6088	Jacob Garcia	1	1	6	0	0.04
6089	Jacob Orr	1	1	6	0	0.04
6090	Jacqueline Obrien	1	1	6	0	0.04
6091	James Cooper	1	1	6	0	0.04
6092	James Nolan	1	1	6	0	0.04
6093	Jason Mayer	1	1	6	0	0.04
6094	Jeffrey Lee	1	1	6	0	0.04
6095	Jeffrey Miller	1	1	6	0	0.04
6096	Jennifer Norris	1	1	6	0	0.04
6097	Jennifer Parker	1	1	6	0	0.04
6098	Jermaine York	1	1	6	0	0.04
6099	Jing Fu	1	1	6	0	0.04
6100	John Bates	1	1	6	0	0.04
6101	John Hopkins	1	1	6	0	0.04
6102	John Howe	1	1	6	0	0.04
6103	Jonathan Snyder	1	1	6	0	0.04
6104	Joseph Navarro	1	1	6	0	0.04
6105	Joseph Rey	1	1	6	0	0.04
6106	Jun Okada	1	1	6	0	0.04
6107	Jérôme Blanchet	1	1	6	0	0.04
6108	Karen White	1	1	6	0	0.04
6109	Kari Harvey	1	1	6	0	0.04
6110	Kathryn Garcia	1	1	6	0	0.04
6111	Kayla Holmes	1	1	6	0	0.04
6112	Kelly Jackson	1	1	6	0	0.04
6113	Kevin Clark	1	1	6	0	0.04
6114	Kimaya Srinivasan	1	1	6	0	0.04
6115	Kimberly Buchanan	1	1	6	0	0.04
6116	Kristina Jackson	1	1	6	0	0.04
6117	Lei Tian	1	1	6	0	0.04
6118	Letizia Agnesi-Piacentini	1	1	6	0	0.04
6119	Liesel Aumann	1	1	6	0	0.04
6120	Linda Garza	1	1	6	0	0.04
6121	Mandy Mendoza	1	1	6	0	0.04
6122	Manuel Murphy	1	1	6	0	0.04
6123	Marc Arroyo	1	1	6	0	0.04
6124	Marco Kusch	1	1	6	0	0.04
6125	Mark Shepard	1	1	6	0	0.04
6126	Martin Ramirez	1	1	6	0	0.04
6127	Mary Gorlitz	1	1	6	0	0.04
6128	Mary Harris	1	1	6	0	0.04
6129	Mary Lewis	1	1	6	0	0.04
6130	Matthew Lopez	1	1	6	0	0.04
6131	Matthew Mclean	1	1	6	0	0.04
6132	Matthew Scott	1	1	6	0	0.04
6133	Maurice Lacroix	1	1	6	0	0.04
6134	Melissa Keller	1	1	6	0	0.04
6135	Melissa Matthews	1	1	6	0	0.04
6136	Melissa Moore	1	1	6	0	0.04
6137	Michael Mitchell	1	1	6	0	0.04
6138	Michael Washington	1	1	6	0	0.04
6139	Micheal Collins	1	1	6	0	0.04
6140	Michele Johnson	1	1	6	0	0.04
6141	Michelle Blair	1	1	6	0	0.04
6142	Ming Ma	1	1	6	0	0.04
6143	Minoru Ikeda	1	1	6	0	0.04
6144	Miraan Ramachandran	1	1	6	0	0.04
6145	Morgan Rodriguez	1	1	6	0	0.04
6146	Na Fang	1	1	6	0	0.04
6147	Na Jiang	1	1	6	0	0.04
6148	Na Shen	1	1	6	0	0.04
6149	Na Zheng	1	1	6	0	0.04
6150	Nathan Carrillo	1	1	6	0	0.04
6151	Nichole Cruz	1	1	6	0	0.04
6152	Nicole Jackson	1	1	6	0	0.04
6153	Nicole Lamb	1	1	6	0	0.04
6154	Noël Loiseau	1	1	6	0	0.04
6155	Omar Lewis	1	1	6	0	0.04
6156	Onkar Kata	1	1	6	0	0.04
6157	Ottavio Barcella-Ritacca	1	1	6	0	0.04
6158	Paolo Gotthard	1	1	6	0	0.04
6159	Patrick Lewis	1	1	6	0	0.04
6160	Patrick Simon	1	1	6	0	0.04
6161	Paul Brown	1	1	6	0	0.04
6162	Pierluigi Tornatore	1	1	6	0	0.04
6163	Ping Wei	1	1	6	0	0.04
6164	Ping Zou	1	1	6	0	0.04
6165	Pomponio Pometta	1	1	6	0	0.04
6166	Pranay Sarin	1	1	6	0	0.04
6167	Qiang Chen	1	1	6	0	0.04
6168	Rachel Carroll	1	1	6	0	0.04
6169	Rania Deol	1	1	6	0	0.04
6170	Rebecca Marshall	1	1	6	0	0.04
6171	Renée Tanguy-Henry	1	1	6	0	0.04
6172	Ritvik Lanka	1	1	6	0	0.04
6173	Robert Santiago	1	1	6	0	0.04
6174	Robert Shaw	1	1	6	0	0.04
6175	Ryan Devan	1	1	6	0	0.04
6176	Ryan Roberts	1	1	6	0	0.04
6177	Serafina Vives-Somoza	1	1	6	0	0.04
6178	Shannon Allen	1	1	6	0	0.04
6179	Siegmar Heintze	1	1	6	0	0.04
6180	Silvia Baum	1	1	6	0	0.04
6181	Stephen Decker	1	1	6	0	0.04
6182	Susan Holmes	1	1	6	0	0.04
6183	Tami Cox	1	1	6	0	0.04
6184	Tammy Turner	1	1	6	0	0.04
6185	Tammy Williams	1	1	6	0	0.04
6186	Tanya Webb	1	1	6	0	0.04
6187	Tao Wan	1	1	6	0	0.04
6188	Tara Kota	1	1	6	0	0.04
6189	Teresa Ellis	1	1	6	0	0.04
6190	Thibault Ollivier	1	1	6	0	0.04
6191	Thomas Alvarez	1	1	6	0	0.04
6192	Thomas Le	1	1	6	0	0.04
6193	Tina Cook	1	1	6	0	0.04
6194	Tiya Sani	1	1	6	0	0.04
6195	Tracy Jones	1	1	6	0	0.04
6196	Tracy Wright	1	1	6	0	0.04
6197	Travis Hill	1	1	6	0	0.04
6198	Valerij Bonbach	1	1	6	0	0.04
6199	Valerio Carullo	1	1	6	0	0.04
6200	Vinnie Cole	1	1	6	0	0.04
6201	Walter Sexton	1	1	6	0	0.04
6202	Wayne Wilson	1	1	6	0	0.04
6203	William Sullivan	1	1	6	0	0.04
6204	Xiuying Gong	1	1	6	0	0.04
6205	Xiuying He	1	1	6	0	0.04
6206	Yang Hu	1	1	6	0	0.04
6207	Yang Jia	1	1	6	0	0.04
6208	Yuvraj Dar	1	1	6	0	0.04
6209	Élise François-Berthelot	1	1	6	0	0.04
6210	Andrew Juarez	1	1	2	1	0.04
6211	Brass Horizon Ensemble	1	1	2	1	0.04
6212	Brendan Hawkins	1	1	2	1	0.04
6213	Brittany Simon	1	1	2	1	0.04
6214	Bruno Majtczak	1	1	2	1	0.04
6215	Cezary Siedlarz	1	1	2	1	0.04
6216	Claus-Dieter Werner-Haering	1	1	2	1	0.04
6217	Elizabeth Flores	1	1	2	1	0.04
6218	Elizabeth Rivera	1	1	2	1	0.04
6219	Halil Neuschäfer	1	1	2	1	0.04
6220	Hannes Cichorius-Seifert	1	1	2	1	0.04
6221	Hélène Lecomte-Techer	1	1	2	1	0.04
6222	Jason Mendoza	1	1	2	1	0.04
6223	Jennifer Henderson	1	1	2	1	0.04
6224	Jeremy Bryant	1	1	2	1	0.04
6225	Josephine Kabus	1	1	2	1	0.04
6226	Kendra Spears	1	1	2	1	0.04
6227	Lisa Hines	1	1	2	1	0.04
6228	Matthew Price	1	1	2	1	0.04
6229	Michael Moreno	1	1	2	1	0.04
6230	Michael Perkins	1	1	2	1	0.04
6231	Midnight Terrain	1	1	2	1	0.04
6232	Min Song	1	1	2	1	0.04
6233	Neelofar Balan	1	1	2	1	0.04
6234	Robert Carlson	1	1	2	1	0.04
6235	Ronald Warner	1	1	2	1	0.04
6236	Samiha Aurora	1	1	2	1	0.04
6237	Susan Garcia	1	1	2	1	0.04
6238	Violetta Peukert	1	1	2	1	0.04
6239	Wei Hao	1	1	2	1	0.04
6240	Aarav Balan	1	1	5	0	0.04
6241	Adam Reid	1	1	5	0	0.04
6242	Advik Shroff	1	1	5	0	0.04
6243	Alexis Vincent	1	1	5	0	0.04
6244	Alicia Rice	1	1	5	0	0.04
6245	Allison Barnes	1	1	5	0	0.04
6246	Amanda Black	1	1	5	0	0.04
6247	Amanda English	1	1	5	0	0.04
6248	Amanda Moore	1	1	5	0	0.04
6249	Amber Smith	1	1	5	0	0.04
6250	Amy Rodriguez	1	1	5	0	0.04
6251	Ana Collins	1	1	5	0	0.04
6252	Andrea Duncan	1	1	5	0	0.04
6253	Andrew Montes	1	1	5	0	0.04
6254	Angela Black	1	1	5	0	0.04
6255	Angela Rios	1	1	5	0	0.04
6256	Anita Szałata	1	1	5	0	0.04
6257	Annelise Heidrich	1	1	5	0	0.04
6258	Anthony Adams	1	1	5	0	0.04
6259	Anthony Bailey	1	1	5	0	0.04
6260	Anthony Wheeler	1	1	5	0	0.04
6261	Arnav Saran	1	1	5	0	0.04
6262	Aroa Coca	1	1	5	0	0.04
6263	Arthur Lewis	1	1	5	0	0.04
6264	Ashley Hernandez	1	1	5	0	0.04
6265	Ashley Moore	1	1	5	0	0.04
6266	Auguste Perrin	1	1	5	0	0.04
6267	Bekir Ladeck	1	1	5	0	0.04
6268	Ben Ponci	1	1	5	0	0.04
6269	Benjamin Leon	1	1	5	0	0.04
6270	Beverly Johnson	1	1	5	0	0.04
6271	Bianca Ward	1	1	5	0	0.04
6272	Bradley Small	1	1	5	0	0.04
6273	Brandi Price	1	1	5	0	0.04
6274	Brian Le	1	1	5	0	0.04
6275	Briana Johnson	1	1	5	0	0.04
6276	Brianna Williams	1	1	5	0	0.04
6277	Brittany Wright	1	1	5	0	0.04
6278	Camillo Liguori-Pistoletto	1	1	5	0	0.04
6279	Carl Serrano	1	1	5	0	0.04
6280	Carlos Anderson	1	1	5	0	0.04
6281	Carlos Davis	1	1	5	0	0.04
6282	Carrie Webb	1	1	5	0	0.04
6283	Cassandra Hernandez	1	1	5	0	0.04
6284	Cathrin Bruder	1	1	5	0	0.04
6285	Chao Kang	1	1	5	0	0.04
6286	Chao Xia	1	1	5	0	0.04
6287	Charles Horton	1	1	5	0	0.04
6288	Christine Anderson	1	1	5	0	0.04
6289	Christopher Clark	1	1	5	0	0.04
6290	Christopher Fox	1	1	5	0	0.04
6291	Christopher Richards	1	1	5	0	0.04
6292	Connie Turner	1	1	5	0	0.04
6293	Corrado Chinnici-Ferrabosco	1	1	5	0	0.04
6294	Cory Carr	1	1	5	0	0.04
6295	Cynthia Lewis	1	1	5	0	0.04
6296	Cynthia Nash	1	1	5	0	0.04
6297	Daniel Campbell	1	1	5	0	0.04
6298	Daria Ceravolo	1	1	5	0	0.04
6299	Darko Kambs	1	1	5	0	0.04
6300	Dave Miller	1	1	5	0	0.04
6301	David Hebert	1	1	5	0	0.04
6302	Denise Moody	1	1	5	0	0.04
6303	Denise Price	1	1	5	0	0.04
6304	Derek Baxter	1	1	5	0	0.04
6305	Dharmajan Lad	1	1	5	0	0.04
6306	Donna Christensen	1	1	5	0	0.04
6307	Douglas Nguyen	1	1	5	0	0.04
6308	Elizabeth Lin	1	1	5	0	0.04
6309	Emily Merritt	1	1	5	0	0.04
6310	Emma Venditti-Brugnaro	1	1	5	0	0.04
6311	Eric Sanchez	1	1	5	0	0.04
6312	Erica Poole	1	1	5	0	0.04
6313	Eva Ray	1	1	5	0	0.04
6314	Evan Solis	1	1	5	0	0.04
6315	Evangelista Muti	1	1	5	0	0.04
6316	Fang Peng	1	1	5	0	0.04
6317	Fito Pacheco León	1	1	5	0	0.04
6318	Fractured Mirrors	1	1	5	0	0.04
6319	Frau Evelyn Schmiedt	1	1	5	0	0.04
6320	Fryderyk Deptuch	1	1	5	0	0.04
6321	Gary Edwards	1	1	5	0	0.04
6322	Gatik Gandhi	1	1	5	0	0.04
6323	Gatik Thaker	1	1	5	0	0.04
6324	George Russell	1	1	5	0	0.04
6325	Gerhardt Jäkel	1	1	5	0	0.04
6326	Gerold Boucsein-Oestrovsky	1	1	5	0	0.04
6327	Geronimo Morpurgo	1	1	5	0	0.04
6328	Gokul Dhaliwal	1	1	5	0	0.04
6329	Gregory Romero	1	1	5	0	0.04
6330	Hannah Wilkinson	1	1	5	0	0.04
6331	Harri Oderwald	1	1	5	0	0.04
6332	Hector Jones	1	1	5	0	0.04
6333	Heinz-Dieter Pruschke	1	1	5	0	0.04
6334	Henry Norris	1	1	5	0	0.04
6335	Ibán Salazar	1	1	5	0	0.04
6336	Indrajit Halder	1	1	5	0	0.04
6337	Isidor Hethur	1	1	5	0	0.04
6338	Ivo Cannizzaro	1	1	5	0	0.04
6339	James Perry	1	1	5	0	0.04
6340	James Rice	1	1	5	0	0.04
6341	Jamie Walker	1	1	5	0	0.04
6342	Jaroslava Hrubá	1	1	5	0	0.04
6343	Jasmin Rogner	1	1	5	0	0.04
6344	Jason Hendricks	1	1	5	0	0.04
6345	Jayan Chawla	1	1	5	0	0.04
6346	Jeffery Mitchell	1	1	5	0	0.04
6347	Jennifer Meadows	1	1	5	0	0.04
6348	Jennifer Olson	1	1	5	0	0.04
6349	Jessica Fry	1	1	5	0	0.04
6350	Jie Bai	1	1	5	0	0.04
6351	Jie Tang	1	1	5	0	0.04
6352	Jindřich Zeman	1	1	5	0	0.04
6353	Jing Xiong	1	1	5	0	0.04
6354	Jodi Morgan	1	1	5	0	0.04
6355	John Cortez	1	1	5	0	0.04
6356	John Kemp	1	1	5	0	0.04
6357	John Wheeler	1	1	5	0	0.04
6358	Joseph Cole	1	1	5	0	0.04
6359	Joseph Walton	1	1	5	0	0.04
6360	Joshua Best	1	1	5	0	0.04
6361	Jun Lu	1	1	5	0	0.04
6362	Jun Ren	1	1	5	0	0.04
6363	Jun Sun	1	1	5	0	0.04
6364	Kaira Saxena	1	1	5	0	0.04
6365	Kana Ito	1	1	5	0	0.04
6366	Karen Sanchez	1	1	5	0	0.04
6367	Karla Säuberlich-Reichmann	1	1	5	0	0.04
6368	Katelyn Chavez	1	1	5	0	0.04
6369	Katherine Dodson	1	1	5	0	0.04
6370	Katherine Flores	1	1	5	0	0.04
6371	Katherine Gutierrez	1	1	5	0	0.04
6372	Katherine Ortega	1	1	5	0	0.04
6373	Katherine Smith	1	1	5	0	0.04
6374	Kathy Castro	1	1	5	0	0.04
6375	Katie Bryant	1	1	5	0	0.04
6376	Kelli Martinez	1	1	5	0	0.04
6377	Kelly Boyd	1	1	5	0	0.04
6378	Kelly Dickson	1	1	5	0	0.04
6379	Kick van Geffen	1	1	5	0	0.04
6380	Kimberly House	1	1	5	0	0.04
6381	Krystyna Szklarek	1	1	5	0	0.04
6382	Kunigunda Losekann	1	1	5	0	0.04
6383	Lando Bulzoni	1	1	5	0	0.04
6384	Larry Patel	1	1	5	0	0.04
6385	Lauretta Vittadello	1	1	5	0	0.04
6386	Lei Ding	1	1	5	0	0.04
6387	Lei Han	1	1	5	0	0.04
6388	Lei Mo	1	1	5	0	0.04
6389	Lei Ye	1	1	5	0	0.04
6390	Leonard Schwital	1	1	5	0	0.04
6391	Li Qiao	1	1	5	0	0.04
6392	Liam de Vrome	1	1	5	0	0.04
6393	Liesbeth Köhler	1	1	5	0	0.04
6394	Lindsey Simpson	1	1	5	0	0.04
6395	Lisa Payne	1	1	5	0	0.04
6396	Lisa Pennington	1	1	5	0	0.04
6397	Liza Mahieu-Groenestein	1	1	5	0	0.04
6398	Liza Rijcken	1	1	5	0	0.04
6399	Lucia Ceci	1	1	5	0	0.04
6400	Mackenzie Compton	1	1	5	0	0.04
6401	Madhup Banerjee	1	1	5	0	0.04
6402	Magdalene Rust	1	1	5	0	0.04
6403	Mannat Chad	1	1	5	0	0.04
6404	Maria Romero	1	1	5	0	0.04
6405	Marietta Kraushaar	1	1	5	0	0.04
6406	Mark Martinez	1	1	5	0	0.04
6407	Mark Mueller	1	1	5	0	0.04
6408	Mark Nunez	1	1	5	0	0.04
6409	Mathias Tröst	1	1	5	0	0.04
6410	Mathilde Ribeiro	1	1	5	0	0.04
6411	Matthew Burton	1	1	5	0	0.04
6412	Matthew Collins	1	1	5	0	0.04
6413	Matthew Moyer	1	1	5	0	0.04
6414	Melanie Ballard	1	1	5	0	0.04
6415	Michael Martinez	1	1	5	0	0.04
6416	Michael Patton	1	1	5	0	0.04
6417	Michael Pineda	1	1	5	0	0.04
6418	Michelle Aguirre	1	1	5	0	0.04
6419	Miki Kondo	1	1	5	0	0.04
6420	Miloš Horváth	1	1	5	0	0.04
6421	Mitchell Ross	1	1	5	0	0.04
6422	Na Du	1	1	5	0	0.04
6423	Na Lei	1	1	5	0	0.04
6424	Natalie Hernandez	1	1	5	0	0.04
6425	Natalie Wood	1	1	5	0	0.04
6426	Nermin Förster	1	1	5	0	0.04
6427	Nichole Dean	1	1	5	0	0.04
6428	Nico Casalodi	1	1	5	0	0.04
6429	Nicole Thomas	1	1	5	0	0.04
6430	Ojas Ramakrishnan	1	1	5	0	0.04
6431	Orazio Curatoli	1	1	5	0	0.04
6432	Paige Mitchell	1	1	5	0	0.04
6433	Pamela Reynolds	1	1	5	0	0.04
6434	Peter Arnold	1	1	5	0	0.04
6435	Peter Hughes	1	1	5	0	0.04
6436	Piermaria Bevilacqua-Altera	1	1	5	0	0.04
6437	Pihu Sunder	1	1	5	0	0.04
6438	Ping Qian	1	1	5	0	0.04
6439	Priyansh Bahl	1	1	5	0	0.04
6440	Puccio Farnese	1	1	5	0	0.04
6441	Qiang Qiu	1	1	5	0	0.04
6442	Qiang Yi	1	1	5	0	0.04
6443	Rachel Ford	1	1	5	0	0.04
6444	Radomír Hruška	1	1	5	0	0.04
6445	Rebecca Thompson	1	1	5	0	0.04
6446	Renee Matthews	1	1	5	0	0.04
6447	Richard Cook	1	1	5	0	0.04
6448	Robert White	1	1	5	0	0.04
6449	Robert Williams	1	1	5	0	0.04
6450	Robin Miller	1	1	5	0	0.04
6451	Roger Morgan	1	1	5	0	0.04
6452	Rolf Pölitz	1	1	5	0	0.04
6453	Ronald Johnson	1	1	5	0	0.04
6454	Ronald Santos	1	1	5	0	0.04
6455	Rosmarie Losekann	1	1	5	0	0.04
6456	Roy Dominguez	1	1	5	0	0.04
6457	Ryan Conley	1	1	5	0	0.04
6458	Ryan van 't Houteveen-Luboch	1	1	5	0	0.04
6459	Sara Ramirez	1	1	5	0	0.04
6460	Sarah Brown	1	1	5	0	0.04
6461	Sarah Collins	1	1	5	0	0.04
6462	Scott Daniel	1	1	5	0	0.04
6463	Severo Gimenez Ródenas	1	1	5	0	0.04
6464	Shalv Sarin	1	1	5	0	0.04
6465	Shanaya Dave	1	1	5	0	0.04
6466	Sharon Holland	1	1	5	0	0.04
6467	Shayak Sen	1	1	5	0	0.04
6468	Sherry Flores	1	1	5	0	0.04
6469	Shray Ranganathan	1	1	5	0	0.04
6470	Sierra Gibson	1	1	5	0	0.04
6471	Silva Speer	1	1	5	0	0.04
6472	Stella Fusani	1	1	5	0	0.04
6473	Stephen Pope	1	1	5	0	0.04
6474	Sue May	1	1	5	0	0.04
6475	Susan Briggs	1	1	5	0	0.04
6476	Susan Evans	1	1	5	0	0.04
6477	Suzanne Bonilla	1	1	5	0	0.04
6478	Tao Cao	1	1	5	0	0.04
6479	Tao Liang	1	1	5	0	0.04
6480	Tao Wang	1	1	5	0	0.04
6481	Tara Basu	1	1	5	0	0.04
6482	Tarini Bora	1	1	5	0	0.04
6483	Taylor Miller	1	1	5	0	0.04
6484	Taylor Perry	1	1	5	0	0.04
6485	Thierry Fernandez Le Bègue	1	1	5	0	0.04
6486	Tiffany Kelly	1	1	5	0	0.04
6487	Tracy Warner	1	1	5	0	0.04
6488	Tracy Warren	1	1	5	0	0.04
6489	Trevor Freeman	1	1	5	0	0.04
6490	Tyler Lee	1	1	5	0	0.04
6491	Tyler Martinez	1	1	5	0	0.04
6492	Ugolino Bettin	1	1	5	0	0.04
6493	Victoire Roger-Noël	1	1	5	0	0.04
6494	Victor Scott	1	1	5	0	0.04
6495	Virginia Stuart	1	1	5	0	0.04
6496	Vlastimil Pokorný	1	1	5	0	0.04
6497	Wanda Fuller	1	1	5	0	0.04
6498	Wei Fang	1	1	5	0	0.04
6499	Wei Fu	1	1	5	0	0.04
6500	Wei Xiang	1	1	5	0	0.04
6501	Wendy Ayala	1	1	5	0	0.04
6502	Whitney Tucker	1	1	5	0	0.04
6503	Whitney Williams	1	1	5	0	0.04
6504	William Donovan	1	1	5	0	0.04
6505	Xiuying Liu	1	1	5	0	0.04
6506	Xiuying Shao	1	1	5	0	0.04
6507	Xiuying Wu	1	1	5	0	0.04
6508	Yan Huang	1	1	5	0	0.04
6509	Yang Wang	1	1	5	0	0.04
6510	Yolanda Hernandez	1	1	5	0	0.04
6511	Yong Cai	1	1	5	0	0.04
6512	Yong Peng	1	1	5	0	0.04
6513	Zachary Ellis	1	1	5	0	0.04
6514	Zaira Peano-Castelli	1	1	5	0	0.04
6515	Zaira Verga-Cannizzaro	1	1	5	0	0.04
6516	Zeeshan Manda	1	1	5	0	0.04
6517	Deborah Hayes	1	0	11	3	0.04
6518	Pihu Chowdhury	1	0	11	3	0.04
6519	Stephanie Wilson	1	0	11	3	0.04
6520	Amber Ward	1	1	1	1	0.04
6521	Anay Dey	1	1	1	1	0.04
6522	Anna Maria Michel	1	1	1	1	0.04
6523	Aurora Calarco	1	1	1	1	0.04
6524	Daniel Bluszcz	1	1	1	1	0.04
6525	Janina Worach	1	1	1	1	0.04
6526	Joshua Taylor	1	1	1	1	0.04
6527	Shannon Harvey	1	1	1	1	0.04
6528	Veronica Burns	1	1	1	1	0.04
6529	Stephanie Roberts	1	0	14	2	0.04
6530	Juno Ray	2	0	5	0	0.04
6531	Savannah Teal	2	0	5	0	0.04
6532	Aaron Carter	1	1	4	0	0.04
6533	Abigail Hawkins	1	1	4	0	0.04
6534	Abigaíl del Franch	1	1	4	0	0.04
6535	Abril Serna	1	1	4	0	0.04
6536	Adele Curatoli	1	1	4	0	0.04
6537	Adira Bhagat	1	1	4	0	0.04
6538	Adrian Blevins	1	1	4	0	0.04
6539	Adrien Gaudin	1	1	4	0	0.04
6540	Adrienne Godard	1	1	4	0	0.04
6541	Alain du Vaillant	1	1	4	0	0.04
6542	Alejandro Baker	1	1	4	0	0.04
6543	Alex Schwartz	1	1	4	0	0.04
6544	Alex Smith	1	1	4	0	0.04
6545	Alexander Curry Jr.	1	1	4	0	0.04
6546	Alexandra Montes	1	1	4	0	0.04
6547	Alicia Jordan	1	1	4	0	0.04
6548	Allison Curtis	1	1	4	0	0.04
6549	Allison Marshall	1	1	4	0	0.04
6550	Alyssa Adams	1	1	4	0	0.04
6551	Amanda Baker	1	1	4	0	0.04
6552	Amanda Coleman	1	1	4	0	0.04
6553	Amanda Hardy	1	1	4	0	0.04
6554	Amber Sanders	1	1	4	0	0.04
6555	Amy Carroll	1	1	4	0	0.04
6556	Amy Johnston	1	1	4	0	0.04
6557	Anastasios Hölzenbecher-Henschel	1	1	4	0	0.04
6558	Anay Boase	1	1	4	0	0.04
6559	Anaïs Blanchard	1	1	4	0	0.04
6560	Andrew Cruz	1	1	4	0	0.04
6561	Andrew Lee	1	1	4	0	0.04
6562	Andy Eigenwillig	1	1	4	0	0.04
6563	Aneta Mies-Fritsch	1	1	4	0	0.04
6564	Anica Baum	1	1	4	0	0.04
6565	Anthony Burns	1	1	4	0	0.04
6566	Anthony Hamilton	1	1	4	0	0.04
6567	Anthony Padilla	1	1	4	0	0.04
6568	Anthony Silva	1	1	4	0	0.04
6569	Antonia Balotelli	1	1	4	0	0.04
6570	Ashlee White	1	1	4	0	0.04
6571	Beatrix Mende	1	1	4	0	0.04
6572	Benjamin Chen	1	1	4	0	0.04
6573	Benjamin Valencia	1	1	4	0	0.04
6574	Bernardita Marisol Marqués Sosa	1	1	4	0	0.04
6575	Beverly Pham	1	1	4	0	0.04
6576	Bobby Walters	1	1	4	0	0.04
6577	Brandon Dillon	1	1	4	0	0.04
6578	Brandon Flores	1	1	4	0	0.04
6579	Brandon Marshall	1	1	4	0	0.04
6580	Brent White	1	1	4	0	0.04
6581	Brett Mcdonald	1	1	4	0	0.04
6582	Brian Garrett	1	1	4	0	0.04
6583	Brian Hall	1	1	4	0	0.04
6584	Brian Hamilton	1	1	4	0	0.04
6585	Brian Mcbride	1	1	4	0	0.04
6586	Brian Pratt	1	1	4	0	0.04
6587	Brianna Gray	1	1	4	0	0.04
6588	Brittany Oliver	1	1	4	0	0.04
6589	Brittney Rivera	1	1	4	0	0.04
6590	Brooke Robinson	1	1	4	0	0.04
6591	Bruce Young	1	1	4	0	0.04
6592	Bryan Laffray-Recers	1	1	4	0	0.04
6593	Caleb Hunter	1	1	4	0	0.04
6594	Calvin Quinn	1	1	4	0	0.04
6595	Carly Luna	1	1	4	0	0.04
6596	Carol Wallace	1	1	4	0	0.04
6597	Caroline Lewis	1	1	4	0	0.04
6598	Caroline Williams	1	1	4	0	0.04
6599	Carolyn Ramos	1	1	4	0	0.04
6600	Carrie Griffin	1	1	4	0	0.04
6601	Casey Perez	1	1	4	0	0.04
6602	Cathy Oconnor	1	1	4	0	0.04
6603	Cemal Boucsein	1	1	4	0	0.04
6604	Chad Gibson	1	1	4	0	0.04
6605	Chao Long	1	1	4	0	0.04
6606	Chao Lu	1	1	4	0	0.04
6607	Charles White	1	1	4	0	0.04
6608	Charlotte Escobar	1	1	4	0	0.04
6609	Chelsea Harris	1	1	4	0	0.04
6610	Chelsea Liu	1	1	4	0	0.04
6611	Cheryl Miller	1	1	4	0	0.04
6612	Cheryl Peterson	1	1	4	0	0.04
6613	Chris Ward	1	1	4	0	0.04
6614	Christy Hodges	1	1	4	0	0.04
6615	Cilli Trommler	1	1	4	0	0.04
6616	Cindy Moore	1	1	4	0	0.04
6617	Cindy Zamora	1	1	4	0	0.04
6618	Clara Weitzel	1	1	4	0	0.04
6619	Claudine Martin	1	1	4	0	0.04
6620	Colleen Sandoval	1	1	4	0	0.04
6621	Cynthia Moore	1	1	4	0	0.04
6622	Célina Collin de la Chauveau	1	1	4	0	0.04
6623	Dale Suarez	1	1	4	0	0.04
6624	Dana Green	1	1	4	0	0.04
6625	Daniel Kent	1	1	4	0	0.04
6626	David Castaneda	1	1	4	0	0.04
6627	David Chandler	1	1	4	0	0.04
6628	David Hughes	1	1	4	0	0.04
6629	David Knox	1	1	4	0	0.04
6630	David Morgan	1	1	4	0	0.04
6631	Dawn Hopkins	1	1	4	0	0.04
6632	Debbie Edwards	1	1	4	0	0.04
6633	Debra Avery	1	1	4	0	0.04
6634	Devin Chan	1	1	4	0	0.04
6635	Dierk Reichmann	1	1	4	0	0.04
6636	Diya Goyal	1	1	4	0	0.04
6637	Donald Nelson	1	1	4	0	0.04
6638	Donatello Schiaparelli	1	1	4	0	0.04
6639	Douglas Griffin	1	1	4	0	0.04
6640	Duane Richardson	1	1	4	0	0.04
6641	Dylan Rice	1	1	4	0	0.04
6642	Eileen Wiley	1	1	4	0	0.04
6643	Elakshi Shukla	1	1	4	0	0.04
6644	Elena Olivera Agudo	1	1	4	0	0.04
6645	Enno Reinhardt	1	1	4	0	0.04
6646	Eric Boyd	1	1	4	0	0.04
6647	Eric King	1	1	4	0	0.04
6648	Ermanno Rosiello	1	1	4	0	0.04
6649	Ernest Mills	1	1	4	0	0.04
6650	Evelia Viñas-Maldonado	1	1	4	0	0.04
6651	Femke de Koning-Hendriks	1	1	4	0	0.04
6652	Ferdinando Nordio	1	1	4	0	0.04
6653	Fernanda Lamborghini	1	1	4	0	0.04
6654	Gabriel Rosales	1	1	4	0	0.04
6655	Gang Guo	1	1	4	0	0.04
6656	Gang Lu	1	1	4	0	0.04
6657	Gang Xiao	1	1	4	0	0.04
6658	Gatik Soni	1	1	4	0	0.04
6659	Gemma Gentilini	1	1	4	0	0.04
6660	Geraldo Moreno-Bravo	1	1	4	0	0.04
6661	Gertraud Franke-Trupp	1	1	4	0	0.04
6662	Gianfrancesco Merisi	1	1	4	0	0.04
6663	Giorgia Norbiato	1	1	4	0	0.04
6664	Greco Duse-Liverotti	1	1	4	0	0.04
6665	Guy Voisin	1	1	4	0	0.04
6666	Haley Adams	1	1	4	0	0.04
6667	Heather Dalton	1	1	4	0	0.04
6668	Herr Reinald Roht	1	1	4	0	0.04
6669	Herr Wolf-Rüdiger Werner	1	1	4	0	0.04
6670	Hiran Sarna	1	1	4	0	0.04
6671	Hugo Pareto	1	1	4	0	0.04
6672	Ian Dawson	1	1	4	0	0.04
6673	Ignatz Jüttner	1	1	4	0	0.04
6674	Indranil Bedi	1	1	4	0	0.04
6675	Ippazio Lattuada	1	1	4	0	0.04
6676	Isabel Wilson	1	1	4	0	0.04
6677	Jacob Ramirez	1	1	4	0	0.04
6678	Jacob Richards	1	1	4	0	0.04
6679	Jacob Rogers	1	1	4	0	0.04
6680	Jacqueline Bowen	1	1	4	0	0.04
6681	Jake Bailey	1	1	4	0	0.04
6682	James Hart	1	1	4	0	0.04
6683	Janice Fuentes	1	1	4	0	0.04
6684	Jared Snow	1	1	4	0	0.04
6685	Jason Miller	1	1	4	0	0.04
6686	Jeffrey Butler	1	1	4	0	0.04
6687	Jenna York	1	1	4	0	0.04
6688	Jennifer Adams	1	1	4	0	0.04
6689	Jennifer Boyer	1	1	4	0	0.04
6690	Jennifer Harris	1	1	4	0	0.04
6691	Jennifer Spencer	1	1	4	0	0.04
6692	Jennifer Vang	1	1	4	0	0.04
6693	Jessica Moreno	1	1	4	0	0.04
6694	Jessica Schroeder	1	1	4	0	0.04
6695	Jessica Valentine	1	1	4	0	0.04
6696	Jie Qian	1	1	4	0	0.04
6697	Jill Gardner	1	1	4	0	0.04
6698	Jing Zheng	1	1	4	0	0.04
6699	Jo Stewart	1	1	4	0	0.04
6700	Joanne Fowler	1	1	4	0	0.04
6701	Jodi Gilbert	1	1	4	0	0.04
6702	Jonathan Lawson	1	1	4	0	0.04
6703	Jordan Lawrence	1	1	4	0	0.04
6704	Joseph Allen	1	1	4	0	0.04
6705	Joshua Watson	1	1	4	0	0.04
6706	Juan Han	1	1	4	0	0.04
6707	Judy Elliott	1	1	4	0	0.04
6708	Julia Allen	1	1	4	0	0.04
6709	Juri Textor	1	1	4	0	0.04
6710	Justin Ellison	1	1	4	0	0.04
6711	Kacper Panasewicz	1	1	4	0	0.04
6712	Kaira Dutt	1	1	4	0	0.04
6713	Karen Jackson	1	1	4	0	0.04
6714	Karen Thomas	1	1	4	0	0.04
6715	Karen Valdez	1	1	4	0	0.04
6716	Kathleen Duran	1	1	4	0	0.04
6717	Kathryn Spencer	1	1	4	0	0.04
6718	Kayla Barnes	1	1	4	0	0.04
6719	Kelly Long	1	1	4	0	0.04
6720	Kelly Lopez	1	1	4	0	0.04
6721	Kenneth Banks	1	1	4	0	0.04
6722	Kenneth Douglas	1	1	4	0	0.04
6723	Kevin Cooke	1	1	4	0	0.04
6724	Kevin Pearson	1	1	4	0	0.04
6725	Kevin Ross	1	1	4	0	0.04
6726	Kimberly Hernandez	1	1	4	0	0.04
6727	Kimberly Lopez	1	1	4	0	0.04
6728	Klaus-Günter Bender	1	1	4	0	0.04
6729	Konstantinos Wende	1	1	4	0	0.04
6730	Kristina Cowan	1	1	4	0	0.04
6731	Kristina Martinez	1	1	4	0	0.04
6732	Krystal Moody	1	1	4	0	0.04
6733	Kumiko Saito	1	1	4	0	0.04
6734	Kyle Mason	1	1	4	0	0.04
6735	Laura Badoglio	1	1	4	0	0.04
6736	Laura Davis	1	1	4	0	0.04
6737	Laurie Rodriguez	1	1	4	0	0.04
6738	Lei Chen	1	1	4	0	0.04
6739	Leon Hrabia	1	1	4	0	0.04
6740	Leoncio de Moliner	1	1	4	0	0.04
6741	Lia Grant	1	1	4	0	0.04
6742	Liliana Morellato	1	1	4	0	0.04
6743	Lina Tirabassi	1	1	4	0	0.04
6744	Lindsay Lyons	1	1	4	0	0.04
6745	Lindsey Diaz	1	1	4	0	0.04
6746	Lori Bowers	1	1	4	0	0.04
6747	Ludovica Tamborini-Paolucci	1	1	4	0	0.04
6748	Madhup Ganesh	1	1	4	0	0.04
6749	Magnus Speer	1	1	4	0	0.04
6750	Marcia Florencia Carmona Cuéllar	1	1	4	0	0.04
6751	Margaret Vargas	1	1	4	0	0.04
6752	Marie Munoz	1	1	4	0	0.04
6753	Marilyn Douglas	1	1	4	0	0.04
6754	Mary Boyd	1	1	4	0	0.04
6755	Mary King	1	1	4	0	0.04
6756	Mary Parsons	1	1	4	0	0.04
6757	Matthew Conner PhD	1	1	4	0	0.04
6758	Matthew Dominguez	1	1	4	0	0.04
6759	Matthew Howell	1	1	4	0	0.04
6760	Maurice Harrison	1	1	4	0	0.04
6761	Mees Oude Heer	1	1	4	0	0.04
6762	Melania Filangieri	1	1	4	0	0.04
6763	Mercedes Salata	1	1	4	0	0.04
6764	Michael Baker	1	1	4	0	0.04
6765	Michael Bennett	1	1	4	0	0.04
6766	Michael Benson	1	1	4	0	0.04
6767	Michael Flowers	1	1	4	0	0.04
6768	Michael Glenn	1	1	4	0	0.04
6769	Michael Gregory	1	1	4	0	0.04
6770	Michael Jefferson	1	1	4	0	0.04
6771	Michael Mann	1	1	4	0	0.04
6772	Michael Montgomery	1	1	4	0	0.04
6773	Michael Perez	1	1	4	0	0.04
6774	Michal Žák	1	1	4	0	0.04
6775	Michelle Atkins	1	1	4	0	0.04
6776	Michelle Monnier	1	1	4	0	0.04
6777	Michelle Weiss	1	1	4	0	0.04
6778	Miki Sato	1	1	4	0	0.04
6779	Miluše Dušková	1	1	4	0	0.04
6780	Min Feng	1	1	4	0	0.04
6781	Min Lai	1	1	4	0	0.04
6782	Min Luo	1	1	4	0	0.04
6783	Ming Guo	1	1	4	0	0.04
6784	Miss Jeanette Marshall	1	1	4	0	0.04
6785	Misty Reyes	1	1	4	0	0.04
6786	Mitchell Bryan	1	1	4	0	0.04
6787	Momoko Ito	1	1	4	0	0.04
6788	Monika Bochnia	1	1	4	0	0.04
6789	Myra Devan	1	1	4	0	0.04
6790	Na Wang	1	1	4	0	0.04
6791	Naděžda Pešková	1	1	4	0	0.04
6792	Nancy Wright	1	1	4	0	0.04
6793	Naoko Yamada	1	1	4	0	0.04
6794	Navya Barad	1	1	4	0	0.04
6795	Nicholas Pineda	1	1	4	0	0.04
6796	Nichole Benton	1	1	4	0	0.04
6797	Nicoletta Soffici	1	1	4	0	0.04
6798	Nina Flaiano	1	1	4	0	0.04
6799	Océane Bouchet	1	1	4	0	0.04
6800	Paloma Merisi	1	1	4	0	0.04
6801	Pamela Crane	1	1	4	0	0.04
6802	Pamela Kent	1	1	4	0	0.04
6803	Pamela Mitchell	1	1	4	0	0.04
6804	Paul Johnson	1	1	4	0	0.04
6805	Paul-Louis Benard	1	1	4	0	0.04
6806	Perry Thompson	1	1	4	0	0.04
6807	Phillip Kane	1	1	4	0	0.04
6808	Piermaria Modugno	1	1	4	0	0.04
6809	Ping Feng	1	1	4	0	0.04
6810	Ping Kang	1	1	4	0	0.04
6811	Piya Mann	1	1	4	0	0.04
6812	Qiang Cai	1	1	4	0	0.04
6813	Qiang Gong	1	1	4	0	0.04
6814	Rachel Thompson	1	1	4	0	0.04
6815	Raghav Garde	1	1	4	0	0.04
6816	Ramona Camiscione	1	1	4	0	0.04
6817	Ranbir Balay	1	1	4	0	0.04
6818	Randy Vasquez	1	1	4	0	0.04
6819	Regina Hensley	1	1	4	0	0.04
6820	Rhonda Miller	1	1	4	0	0.04
6821	Ricardo Ward	1	1	4	0	0.04
6822	Robert Harris	1	1	4	0	0.04
6823	Robert Mathis	1	1	4	0	0.04
6824	Robert Quinn	1	1	4	0	0.04
6825	Robin Harris	1	1	4	0	0.04
6826	Roksana Wlaźlak	1	1	4	0	0.04
6827	Romil Sibal	1	1	4	0	0.04
6828	Ronald Parks	1	1	4	0	0.04
6829	Ronald Yates	1	1	4	0	0.04
6830	Roy Miller	1	1	4	0	0.04
6831	Ryan Pollard	1	1	4	0	0.04
6832	Sabina Hrušková	1	1	4	0	0.04
6833	Sabine Ollivier-Morel	1	1	4	0	0.04
6834	Samantha Munoz	1	1	4	0	0.04
6835	Samantha Padilla	1	1	4	0	0.04
6836	Samar Luthra	1	1	4	0	0.04
6837	Samiha Chander	1	1	4	0	0.04
6838	Sana Batta	1	1	4	0	0.04
6839	Sana Sunder	1	1	4	0	0.04
6840	Sara Berger	1	1	4	0	0.04
6841	Sarah Clark	1	1	4	0	0.04
6842	Sarah Gray	1	1	4	0	0.04
6843	Sarah Hunter	1	1	4	0	0.04
6844	Sawyer and the Frontiers	1	1	4	0	0.04
6845	Scott Burke	1	1	4	0	0.04
6846	Scott Garcia	1	1	4	0	0.04
6847	Sean Jackson	1	1	4	0	0.04
6848	Sean Lewis	1	1	4	0	0.04
6849	Sean Mendoza	1	1	4	0	0.04
6850	Sean Phillips	1	1	4	0	0.04
6851	Sean Rogers	1	1	4	0	0.04
6852	Seve Francisco	1	1	4	0	0.04
6853	Sonia Innocenti-Gasperi	1	1	4	0	0.04
6854	Stephanie Johnson	1	1	4	0	0.04
6855	Stephen Vazquez	1	1	4	0	0.04
6856	Steven Calderon	1	1	4	0	0.04
6857	Steven Hobbs	1	1	4	0	0.04
6858	Stéphanie Parent	1	1	4	0	0.04
6859	Susan Brown	1	1	4	0	0.04
6860	Susan Spencer	1	1	4	0	0.04
6861	Susan Wong	1	1	4	0	0.04
6862	Sylwia Skowyra	1	1	4	0	0.04
6863	Taimur Iyer	1	1	4	0	0.04
6864	Tamara Douglas	1	1	4	0	0.04
6865	Tammy Camacho	1	1	4	0	0.04
6866	Tao Shi	1	1	4	0	0.04
6867	Tara Timmermans	1	1	4	0	0.04
6868	Terri Johnson	1	1	4	0	0.04
6869	Terry Nelson	1	1	4	0	0.04
6870	Thekla Schuster	1	1	4	0	0.04
6871	Theresa Jones	1	1	4	0	0.04
6872	Thilo Schmiedt	1	1	4	0	0.04
6873	Thomas Carter	1	1	4	0	0.04
6874	Thomas Palmer	1	1	4	0	0.04
6875	Tiffany Martinez	1	1	4	0	0.04
6876	Tim Slingerland	1	1	4	0	0.04
6877	Timothy Hall	1	1	4	0	0.04
6878	Timothy Padilla	1	1	4	0	0.04
6879	Timothy Robinson	1	1	4	0	0.04
6880	Todd Molina	1	1	4	0	0.04
6881	Tommy Murphy	1	1	4	0	0.04
6882	Urban Budig	1	1	4	0	0.04
6883	Urte Bachmann	1	1	4	0	0.04
6884	Venancio Belletini	1	1	4	0	0.04
6885	Veronica Koch	1	1	4	0	0.04
6886	Vicki Shepard	1	1	4	0	0.04
6887	Victor Miller	1	1	4	0	0.04
6888	Victoria Vazquez	1	1	4	0	0.04
6889	Viviana Escrivá Porcel	1	1	4	0	0.04
6890	Véronique Bernard	1	1	4	0	0.04
6891	Wanda Ray	1	1	4	0	0.04
6892	Wesley Ford	1	1	4	0	0.04
6893	William Ball	1	1	4	0	0.04
6894	William Diaz	1	1	4	0	0.04
6895	Willie Phelps	1	1	4	0	0.04
6896	Xiulan Mao	1	1	4	0	0.04
6897	Xiuying Su	1	1	4	0	0.04
6898	Yan Xiang	1	1	4	0	0.04
6899	Yan Xu	1	1	4	0	0.04
6900	Yasuhiro Yoshida	1	1	4	0	0.04
6901	Yong Xu	1	1	4	0	0.04
6902	Yves Gaudin	1	1	4	0	0.04
6903	Yvonne Morris	1	1	4	0	0.04
6904	Zachary Cole	1	1	4	0	0.04
6905	Émile Didier	1	1	4	0	0.04
6906	Éric Lamy	1	1	4	0	0.04
6907	Éric Louis-Lévy	1	1	4	0	0.04
6908	Aayush Cheema	1	1	3	0	0.03
6909	Adam Allen	1	1	3	0	0.03
6910	Adam Carter	1	1	3	0	0.03
6911	Adam Gill	1	1	3	0	0.03
6912	Adrian Jeszka	1	1	3	0	0.03
6913	Aitor Alcántara Diez	1	1	3	0	0.03
6914	Alain Delannoy	1	1	3	0	0.03
6915	Alan Bray	1	1	3	0	0.03
6916	Aleksander Grześków	1	1	3	0	0.03
6917	Aleksander Sypień	1	1	3	0	0.03
6918	Alex Bridges	1	1	3	0	0.03
6919	Alex Leblanc	1	1	3	0	0.03
6920	Alexander Lawrence	1	1	3	0	0.03
6921	Alexandra King	1	1	3	0	0.03
6922	Alexandra Mcdaniel	1	1	3	0	0.03
6923	Alexandra Scott	1	1	3	0	0.03
6924	Alexei Gotthard	1	1	3	0	0.03
6925	Alexis Moore	1	1	3	0	0.03
6926	Alisha Hartman	1	1	3	0	0.03
6927	Alix Chevallier	1	1	3	0	0.03
6928	Alma Vollbrecht	1	1	3	0	0.03
6929	Amanda Davis	1	1	3	0	0.03
6930	Amber Nguyen	1	1	3	0	0.03
6931	Amber Park	1	1	3	0	0.03
6932	Amber Ramsey	1	1	3	0	0.03
6933	Amber Rodriguez	1	1	3	0	0.03
6934	Amedeo Mimun	1	1	3	0	0.03
6935	Amy Alexander	1	1	3	0	0.03
6936	Amy Barnes	1	1	3	0	0.03
6937	Amy Bright	1	1	3	0	0.03
6938	Amy Campbell	1	1	3	0	0.03
6939	Amy Riley	1	1	3	0	0.03
6940	Amélie Peltier	1	1	3	0	0.03
6941	Andrew Garcia	1	1	3	0	0.03
6942	Andrew Nolan	1	1	3	0	0.03
6943	Angela Collins	1	1	3	0	0.03
6944	Angela Lopez	1	1	3	0	0.03
6945	Angela Perez	1	1	3	0	0.03
6946	Animated Antics	1	1	3	0	0.03
6947	Anna Fechner	1	1	3	0	0.03
6948	Anne Frederick	1	1	3	0	0.03
6949	Anne Verwoert-Slagmolen	1	1	3	0	0.03
6950	Annetta Molesini	1	1	3	0	0.03
6951	Anouk Carre	1	1	3	0	0.03
6952	Anthony Peters	1	1	3	0	0.03
6953	Antonia Steinberg-Löffler	1	1	3	0	0.03
6954	Antonio Morse	1	1	3	0	0.03
6955	Antonio Schroeder	1	1	3	0	0.03
6956	April Mills	1	1	3	0	0.03
6957	April Nelson	1	1	3	0	0.03
6958	Ashley Huang	1	1	3	0	0.03
6959	Ashley Mahoney	1	1	3	0	0.03
6960	Ashley Olsen	1	1	3	0	0.03
6961	Audrey Frye	1	1	3	0	0.03
6962	Auguste de la Laroche	1	1	3	0	0.03
6963	Augusto Franzese	1	1	3	0	0.03
6964	Aurora Vendetti	1	1	3	0	0.03
6965	Aysel Scheibe	1	1	3	0	0.03
6966	Beatrice Albers	1	1	3	0	0.03
6967	Benjamin Dupuis-Lévy	1	1	3	0	0.03
6968	Bernard-Alain Parent	1	1	3	0	0.03
6969	Bernardo Josep Mayol Manjón	1	1	3	0	0.03
6970	Bonnie Roberts	1	1	3	0	0.03
6971	Bradley Anderson	1	1	3	0	0.03
6972	Bradley Gray	1	1	3	0	0.03
6973	Brandi Sutton	1	1	3	0	0.03
6974	Brandon Banks	1	1	3	0	0.03
6975	Brenda Boyd	1	1	3	0	0.03
6976	Brett Terry	1	1	3	0	0.03
6977	Brian Olsen	1	1	3	0	0.03
6978	Brian Oneal	1	1	3	0	0.03
6979	Brian Vincent	1	1	3	0	0.03
6980	Briana Harris	1	1	3	0	0.03
6981	Brianna Hopkins	1	1	3	0	0.03
6982	Brigitte Langlois	1	1	3	0	0.03
6983	Carl Benjamin	1	1	3	0	0.03
6984	Carla Holden	1	1	3	0	0.03
6985	Carolyn Morgan	1	1	3	0	0.03
6986	Catherine Perez	1	1	3	0	0.03
6987	Catherine Roberts	1	1	3	0	0.03
6988	Cato Meyer	1	1	3	0	0.03
6989	Cesar Nixon	1	1	3	0	0.03
6990	Chao Yin	1	1	3	0	0.03
6991	Chelsea Blake	1	1	3	0	0.03
6992	Chirag Bahri	1	1	3	0	0.03
6993	Chris Cook	1	1	3	0	0.03
6994	Christian Gray	1	1	3	0	0.03
6995	Christina Mckay	1	1	3	0	0.03
6996	Christine Brown	1	1	3	0	0.03
6997	Christine Johnson	1	1	3	0	0.03
6998	Christopher Reilly	1	1	3	0	0.03
6999	Christopher Wood	1	1	3	0	0.03
7000	Christopher Young	1	1	3	0	0.03
7001	Chuy Calderon Chaves	1	1	3	0	0.03
7002	Cindy Hernandez	1	1	3	0	0.03
7003	Cindy Parker	1	1	3	0	0.03
7004	Colton Williams	1	1	3	0	0.03
7005	Cory Garrett	1	1	3	0	0.03
7006	Courtney Johnston	1	1	3	0	0.03
7007	Cynthia Jones	1	1	3	0	0.03
7008	Damiano Gelli-Verga	1	1	3	0	0.03
7009	Dana Baker	1	1	3	0	0.03
7010	Dana Medina	1	1	3	0	0.03
7011	Dana Perkins	1	1	3	0	0.03
7012	Daniel Oneill	1	1	3	0	0.03
7013	Daniel Williams	1	1	3	0	0.03
7014	Danny Lindsey	1	1	3	0	0.03
7015	Darshit Mannan	1	1	3	0	0.03
7016	David Choi	1	1	3	0	0.03
7017	David Cruz	1	1	3	0	0.03
7018	David Maddox	1	1	3	0	0.03
7019	David Orr	1	1	3	0	0.03
7020	David Richardson	1	1	3	0	0.03
7021	Dawn Massey	1	1	3	0	0.03
7022	Dawn Mcguire	1	1	3	0	0.03
7023	Deborah Aguilar	1	1	3	0	0.03
7024	Deborah Martinez	1	1	3	0	0.03
7025	Deborah Norton	1	1	3	0	0.03
7026	Deborah Osborne	1	1	3	0	0.03
7027	Debra Sims	1	1	3	0	0.03
7028	Denise Perez	1	1	3	0	0.03
7029	Derek Robinson	1	1	3	0	0.03
7030	Destiny Griffin	1	1	3	0	0.03
7031	Devon White	1	1	3	0	0.03
7032	Dhanush Baria	1	1	3	0	0.03
7033	Diane Anderson	1	1	3	0	0.03
7034	Divij Dora	1	1	3	0	0.03
7035	Dominique Delacruz	1	1	3	0	0.03
7036	Donna Nelson	1	1	3	0	0.03
7037	Doris Hatfield	1	1	3	0	0.03
7038	Eckard Werner	1	1	3	0	0.03
7039	Edward Little	1	1	3	0	0.03
7040	Edward Randolph	1	1	3	0	0.03
7041	Elizabeth Walters	1	1	3	0	0.03
7042	Emily Huynh	1	1	3	0	0.03
7043	Eric Armstrong	1	1	3	0	0.03
7044	Erica Chandler	1	1	3	0	0.03
7045	Erica Herrera	1	1	3	0	0.03
7046	Erik Huang Jr.	1	1	3	0	0.03
7047	Erin Mitchell	1	1	3	0	0.03
7048	Ernest Clark	1	1	3	0	0.03
7049	Ethan Moore	1	1	3	0	0.03
7050	Fang Jin	1	1	3	0	0.03
7051	Fang Wang	1	1	3	0	0.03
7052	Fanny Dörschner	1	1	3	0	0.03
7053	Ferenc Mälzer	1	1	3	0	0.03
7054	Fidel Gras Aller	1	1	3	0	0.03
7055	Flavio del Galán	1	1	3	0	0.03
7056	Flora Casaleggio-Endrizzi	1	1	3	0	0.03
7057	Frederick Jackson Jr.	1	1	3	0	0.03
7058	Fredo Piccinni	1	1	3	0	0.03
7059	Frieda Mitschke	1	1	3	0	0.03
7060	Gang Feng	1	1	3	0	0.03
7061	George Gutierrez	1	1	3	0	0.03
7062	Gerald Collins	1	1	3	0	0.03
7063	Giesela Kuhl	1	1	3	0	0.03
7064	Gina Miller	1	1	3	0	0.03
7065	Gionata Toldo	1	1	3	0	0.03
7066	Greca Canali	1	1	3	0	0.03
7067	Gregory Hansen	1	1	3	0	0.03
7068	Guillaume Cohen du Leduc	1	1	3	0	0.03
7069	Guillaume Petitjean	1	1	3	0	0.03
7070	Guiying Wei	1	1	3	0	0.03
7071	Guiying Zhu	1	1	3	0	0.03
7072	Hannah Hopkins	1	1	3	0	0.03
7073	Heather Kennedy	1	1	3	0	0.03
7074	Heather Velasquez	1	1	3	0	0.03
7075	Heather Wallace	1	1	3	0	0.03
7076	Henrik Seifert	1	1	3	0	0.03
7077	Ida Vigliotti	1	1	3	0	0.03
7078	Ignatz Thanel-Hornich	1	1	3	0	0.03
7079	Ildiko Wesack	1	1	3	0	0.03
7080	Isa Carmichael	1	1	3	0	0.03
7081	Isabella Turner	1	1	3	0	0.03
7082	Isabelle Godfrey van Alemannië	1	1	3	0	0.03
7083	Ise Scardino	1	1	3	0	0.03
7084	Ivo Ziegert	1	1	3	0	0.03
7085	Jackie Johnston	1	1	3	0	0.03
7086	Jacob Arnold	1	1	3	0	0.03
7087	Jacob Gomez	1	1	3	0	0.03
7088	Jacob Romero	1	1	3	0	0.03
7089	Jacqueline Miller	1	1	3	0	0.03
7090	Jacques Imbert de la Munoz	1	1	3	0	0.03
7091	James Calderon	1	1	3	0	0.03
7092	James Middleton	1	1	3	0	0.03
7093	James Simon	1	1	3	0	0.03
7094	Jamie Smith	1	1	3	0	0.03
7095	Jason Chavez	1	1	3	0	0.03
7096	Jason Jones	1	1	3	0	0.03
7097	Jason Mckenzie	1	1	3	0	0.03
7098	Jeanette Bass	1	1	3	0	0.03
7099	Jeff Farmer	1	1	3	0	0.03
7100	Jeffrey Knight	1	1	3	0	0.03
7101	Jennifer Allen	1	1	3	0	0.03
7102	Jennifer Garcia	1	1	3	0	0.03
7103	Jeremy Griffith	1	1	3	0	0.03
7104	Jeremy Jones	1	1	3	0	0.03
7105	Jeremy Russell	1	1	3	0	0.03
7106	Jermaine Gibbs	1	1	3	0	0.03
7107	Jerry Abbott	1	1	3	0	0.03
7108	Jessica Ward	1	1	3	0	0.03
7109	Jessica Wyatt	1	1	3	0	0.03
7110	Jesus Hatfield	1	1	3	0	0.03
7111	Jie Gong	1	1	3	0	0.03
7112	Jie Liao	1	1	3	0	0.03
7113	Jie Luo	1	1	3	0	0.03
7114	Jimmy Arnold	1	1	3	0	0.03
7115	Jing Hao	1	1	3	0	0.03
7116	Jing Peng	1	1	3	0	0.03
7117	Jing Tao	1	1	3	0	0.03
7118	Jivika Keer	1	1	3	0	0.03
7119	Jiya Raman	1	1	3	0	0.03
7120	Jody Jones	1	1	3	0	0.03
7121	Joel Foster	1	1	3	0	0.03
7122	Joerg Schleich	1	1	3	0	0.03
7123	Johannes Linke	1	1	3	0	0.03
7124	John Harris	1	1	3	0	0.03
7125	John May	1	1	3	0	0.03
7126	John Mcdonald	1	1	3	0	0.03
7127	John Rivera	1	1	3	0	0.03
7128	John Taylor	1	1	3	0	0.03
7129	John Watts	1	1	3	0	0.03
7130	John Wright	1	1	3	0	0.03
7131	Jonathan Barker	1	1	3	0	0.03
7132	Jonathan Oestrovsky	1	1	3	0	0.03
7133	Jordan Lutz	1	1	3	0	0.03
7134	Jose Cerdán Paniagua	1	1	3	0	0.03
7135	Jose Manuel Quintana Borrell	1	1	3	0	0.03
7136	Jose Martin	1	1	3	0	0.03
7137	Joseph Dillon	1	1	3	0	0.03
7138	Joseph Jones	1	1	3	0	0.03
7139	Joseph Moore	1	1	3	0	0.03
7140	Joseph Roman	1	1	3	0	0.03
7141	Joseph Stevens	1	1	3	0	0.03
7142	Joseph Thomas	1	1	3	0	0.03
7143	Joseph Villa	1	1	3	0	0.03
7144	Joshua Brewer	1	1	3	0	0.03
7145	Joshua Harris	1	1	3	0	0.03
7146	Joséphine Rodrigues	1	1	3	0	0.03
7147	Juan Shelton	1	1	3	0	0.03
7148	Julie Taylor	1	1	3	0	0.03
7149	Julita Kudełka	1	1	3	0	0.03
7150	Jun Zhao	1	1	3	0	0.03
7151	Justin Morales	1	1	3	0	0.03
7152	Kamil Bílek	1	1	3	0	0.03
7153	Kamil Sura	1	1	3	0	0.03
7154	Karl-Josef Reinhardt	1	1	3	0	0.03
7155	Kathleen Valentine	1	1	3	0	0.03
7156	Kathy Green	1	1	3	0	0.03
7157	Katie Fleming	1	1	3	0	0.03
7158	Katie Richards	1	1	3	0	0.03
7159	Kayla Jenkins	1	1	3	0	0.03
7160	Kayla Roberson	1	1	3	0	0.03
7161	Kazuya Kobayashi	1	1	3	0	0.03
7162	Kazuya Maeda	1	1	3	0	0.03
7163	Kazuya Matsumoto	1	1	3	0	0.03
7164	Kelli Mckinney	1	1	3	0	0.03
7165	Kelly Marshall	1	1	3	0	0.03
7166	Kelly Smith	1	1	3	0	0.03
7167	Kenneth Jimenez	1	1	3	0	0.03
7168	Kenneth Robinson	1	1	3	0	0.03
7169	Kenneth Ross	1	1	3	0	0.03
7170	Kevin Richardson	1	1	3	0	0.03
7171	Kimberly Campbell	1	1	3	0	0.03
7172	Kimberly Guerrero	1	1	3	0	0.03
7173	Kimberly Murphy	1	1	3	0	0.03
7174	Kimberly Thomas	1	1	3	0	0.03
7175	Kirk Kemp	1	1	3	0	0.03
7176	Kreszentia Staude	1	1	3	0	0.03
7177	Krista Collins	1	1	3	0	0.03
7178	Kristen Stanley	1	1	3	0	0.03
7179	Kristina Boyer	1	1	3	0	0.03
7180	Kristy Miller	1	1	3	0	0.03
7181	Kristy Olsen	1	1	3	0	0.03
7182	Laila Rosenow-Mans	1	1	3	0	0.03
7183	Lance Garcia	1	1	3	0	0.03
7184	Lando Parri	1	1	3	0	0.03
7185	Larry Miller	1	1	3	0	0.03
7186	Latasha Watts	1	1	3	0	0.03
7187	Lawrence Guzman	1	1	3	0	0.03
7188	Leah Moore	1	1	3	0	0.03
7189	Lei Hou	1	1	3	0	0.03
7190	Lei Meng	1	1	3	0	0.03
7191	Lei Ren	1	1	3	0	0.03
7192	Lei Song	1	1	3	0	0.03
7193	Leonard Suarez	1	1	3	0	0.03
7194	Li Meng	1	1	3	0	0.03
7195	Lindsay Ponce	1	1	3	0	0.03
7196	Lisa Baker	1	1	3	0	0.03
7197	Lisa Fuentes	1	1	3	0	0.03
7198	Lisa Hanna	1	1	3	0	0.03
7199	Lisa Kirk	1	1	3	0	0.03
7200	Lisa Mccoy	1	1	3	0	0.03
7201	Liwia Cygal	1	1	3	0	0.03
7202	Louis Herman	1	1	3	0	0.03
7203	Luc Hoarau	1	1	3	0	0.03
7204	Luca Gori-Visconti	1	1	3	0	0.03
7205	Luciano Calzada Arcos	1	1	3	0	0.03
7206	Luigina Fibonacci-Caruso	1	1	3	0	0.03
7207	Luisa Tommaseo	1	1	3	0	0.03
7208	Luke Payne	1	1	3	0	0.03
7209	Lupe Neira	1	1	3	0	0.03
7210	Mackenzie Murray	1	1	3	0	0.03
7211	Madeleine Raymond	1	1	3	0	0.03
7212	Madison Rodriguez	1	1	3	0	0.03
7213	Mahika Basu	1	1	3	0	0.03
7214	Manikya Toor	1	1	3	0	0.03
7215	Manjari Bobal	1	1	3	0	0.03
7216	Mannat Edwin	1	1	3	0	0.03
7217	Marcella Belletini	1	1	3	0	0.03
7218	Margherita Giammusso	1	1	3	0	0.03
7219	Margot Becker-Striebitz	1	1	3	0	0.03
7220	Maria Matthews	1	1	3	0	0.03
7221	Marie-Louise Wesack	1	1	3	0	0.03
7222	Marina Trapani	1	1	3	0	0.03
7223	Mariola Heser	1	1	3	0	0.03
7224	Martin Guilbert	1	1	3	0	0.03
7225	Marvin Heuser	1	1	3	0	0.03
7226	Mary Johnson	1	1	3	0	0.03
7227	Mary Walker	1	1	3	0	0.03
7228	Mary White	1	1	3	0	0.03
7229	Matthew Mitchell	1	1	3	0	0.03
7230	Matthäus Hendriks	1	1	3	0	0.03
7231	Megan Marsh	1	1	3	0	0.03
7232	Meike Döhn	1	1	3	0	0.03
7233	Melissa Watkins	1	1	3	0	0.03
7234	Mercedes Torlonia	1	1	3	0	0.03
7235	Michael Barry	1	1	3	0	0.03
7236	Michael Hayes	1	1	3	0	0.03
7237	Michael Johnson	1	1	3	0	0.03
7238	Michael Martin	1	1	3	0	0.03
7239	Michael Mccullough	1	1	3	0	0.03
7240	Michael Tapia	1	1	3	0	0.03
7241	Michael Watson	1	1	3	0	0.03
7242	Micheal Welch	1	1	3	0	0.03
7243	Michele Kim	1	1	3	0	0.03
7244	Michelle Gonzalez	1	1	3	0	0.03
7245	Michelle Johnson	1	1	3	0	0.03
7246	Miki Watanabe	1	1	3	0	0.03
7247	Milada Svobodová	1	1	3	0	0.03
7248	Milo Hendriks	1	1	3	0	0.03
7249	Miranda Robinson	1	1	3	0	0.03
7250	Miss Cynthia Myers	1	1	3	0	0.03
7251	Mitchell Sharp	1	1	3	0	0.03
7252	Mojmír Hruška	1	1	3	0	0.03
7253	Monica Blevins	1	1	3	0	0.03
7254	Monica Murphy	1	1	3	0	0.03
7255	Monica Ramirez	1	1	3	0	0.03
7256	Morgan Carlson	1	1	3	0	0.03
7257	Morgan Shelton	1	1	3	0	0.03
7258	Moritz Keudel	1	1	3	0	0.03
7259	Nakul Gole	1	1	3	0	0.03
7260	Naoto Suzuki	1	1	3	0	0.03
7261	Nath Henry	1	1	3	0	0.03
7262	Nathan Nelson	1	1	3	0	0.03
7263	Nayantara Dugal	1	1	3	0	0.03
7264	Nicholas Gonzalez	1	1	3	0	0.03
7265	Nicholas Mckinney	1	1	3	0	0.03
7266	Nicholas Morgan	1	1	3	0	0.03
7267	Nino Veneziano	1	1	3	0	0.03
7268	Nirvaan Thaker	1	1	3	0	0.03
7269	Odette Dupuy	1	1	3	0	0.03
7270	Omnira	1	1	3	0	0.03
7271	Osamu Yamashita	1	1	3	0	0.03
7272	Oto Macháček	1	1	3	0	0.03
7273	Paloma Fogazzaro	1	1	3	0	0.03
7274	Pamela Ayala	1	1	3	0	0.03
7275	Pamela Elliott	1	1	3	0	0.03
7276	Pamela Massey	1	1	3	0	0.03
7277	Parinaaz Deshmukh	1	1	3	0	0.03
7278	Pascal Pärtzelt-Jockel	1	1	3	0	0.03
7279	Patrick Williams	1	1	3	0	0.03
7280	Paweł Gęca	1	1	3	0	0.03
7281	Peter Knapp	1	1	3	0	0.03
7282	Philip George	1	1	3	0	0.03
7283	Pia Schleich	1	1	3	0	0.03
7284	Pina Garrone	1	1	3	0	0.03
7285	Ping Yang	1	1	3	0	0.03
7286	Rachel Garcia	1	1	3	0	0.03
7287	Rafael Hoz Pizarro	1	1	3	0	0.03
7288	Ralph Bell	1	1	3	0	0.03
7289	Rania Chadha	1	1	3	0	0.03
7290	Raunak Rana	1	1	3	0	0.03
7291	Raymond Hornich-Jacob	1	1	3	0	0.03
7292	Raymond Robinson	1	1	3	0	0.03
7293	Rebecca Schultz	1	1	3	0	0.03
7294	Regina Beck	1	1	3	0	0.03
7295	Rei Sato	1	1	3	0	0.03
7296	Renee Walker	1	1	3	0	0.03
7297	Renske Godfrey van Alemannië	1	1	3	0	0.03
7298	Renée Bègue-Giraud	1	1	3	0	0.03
7299	Rhythm Collective	1	1	3	0	0.03
7300	Richard Francis	1	1	3	0	0.03
7301	Richard Moore	1	1	3	0	0.03
7302	Ricky Richards	1	1	3	0	0.03
7303	Robert Chen	1	1	3	0	0.03
7304	Robert Jefferson	1	1	3	0	0.03
7305	Robert Ryan	1	1	3	0	0.03
7306	Robin Cline	1	1	3	0	0.03
7307	Robin Mayer	1	1	3	0	0.03
7308	Rodolfo Montecchi-Bonomo	1	1	3	0	0.03
7309	Roman Doria	1	1	3	0	0.03
7310	Rosalinde Kreusel-Mühle	1	1	3	0	0.03
7311	Roy Tran	1	1	3	0	0.03
7312	Ruben Watkins	1	1	3	0	0.03
7313	Ryan Morales	1	1	3	0	0.03
7314	Ryan Osborn	1	1	3	0	0.03
7315	Rémy Jourdan	1	1	3	0	0.03
7316	Sabrina Marquez	1	1	3	0	0.03
7317	Saksham Borra	1	1	3	0	0.03
7318	Sandra Miller	1	1	3	0	0.03
7319	Sara Laszuk	1	1	3	0	0.03
7320	Sarah Caldwell	1	1	3	0	0.03
7321	Sarah Riley	1	1	3	0	0.03
7322	Sarah Sims	1	1	3	0	0.03
7323	Savannah Leblanc	1	1	3	0	0.03
7324	Scott Nichols	1	1	3	0	0.03
7325	Scott Schwartz	1	1	3	0	0.03
7326	Selena Gonzalez	1	1	3	0	0.03
7327	Shane Alexander	1	1	3	0	0.03
7328	Shannon Jacobson	1	1	3	0	0.03
7329	Sharon Mitchell	1	1	3	0	0.03
7330	Sheena Lane	1	1	3	0	0.03
7331	Sheila Flores	1	1	3	0	0.03
7332	Sherry Bell	1	1	3	0	0.03
7333	Sherry Mcdaniel	1	1	3	0	0.03
7334	Stacy Moore	1	1	3	0	0.03
7335	Stanislav Prokop	1	1	3	0	0.03
7336	Stephanie Fowler	1	1	3	0	0.03
7337	Stephanie Gray	1	1	3	0	0.03
7338	Stephen Santiago	1	1	3	0	0.03
7339	Steve Bailey	1	1	3	0	0.03
7340	Steven Berg	1	1	3	0	0.03
7341	Steven Weaver	1	1	3	0	0.03
7342	Summer Mahoney	1	1	3	0	0.03
7343	Susan Francis	1	1	3	0	0.03
7344	Susan Randall	1	1	3	0	0.03
7345	Susan Rodriguez	1	1	3	0	0.03
7346	Susanna Raimondi	1	1	3	0	0.03
7347	Suzanne Martinez	1	1	3	0	0.03
7348	Tammy Campos	1	1	3	0	0.03
7349	Tammy Howell	1	1	3	0	0.03
7350	Tao Zhong	1	1	3	0	0.03
7351	Taro Mori	1	1	3	0	0.03
7352	Taro Sakamoto	1	1	3	0	0.03
7353	Tatjana Gieß	1	1	3	0	0.03
7354	Taylor Hayes	1	1	3	0	0.03
7355	Taylor Martin	1	1	3	0	0.03
7356	Teresa Nelson	1	1	3	0	0.03
7357	Terry Adams	1	1	3	0	0.03
7358	Theodore Simmons	1	1	3	0	0.03
7359	Theresa Johnson	1	1	3	0	0.03
7360	Thomas Meyer	1	1	3	0	0.03
7361	Tim Morgan	1	1	3	0	0.03
7362	Timothy Sanders	1	1	3	0	0.03
7363	Timothée Charles	1	1	3	0	0.03
7364	Tina Hart	1	1	3	0	0.03
7365	Tina Morgan	1	1	3	0	0.03
7366	Tina Spencer	1	1	3	0	0.03
7367	Todd Thompson	1	1	3	0	0.03
7368	Tonya May	1	1	3	0	0.03
7369	Tonya Wright	1	1	3	0	0.03
7370	Travis Price	1	1	3	0	0.03
7371	Trevor Singh	1	1	3	0	0.03
7372	Tyler Case	1	1	3	0	0.03
7373	Tyler Clay	1	1	3	0	0.03
7374	Ulrich Klotz	1	1	3	0	0.03
7375	Valerie Pena	1	1	3	0	0.03
7376	Valérie Valette	1	1	3	0	0.03
7377	Vicenta Castañeda Arroyo	1	1	3	0	0.03
7378	Victoria Velazquez	1	1	3	0	0.03
7379	Wei Ren	1	1	3	0	0.03
7380	Wei Yan	1	1	3	0	0.03
7381	Wendy Nelson	1	1	3	0	0.03
7382	Whitney Schmidt	1	1	3	0	0.03
7383	William Delacruz	1	1	3	0	0.03
7384	William Francis	1	1	3	0	0.03
7385	William Shields	1	1	3	0	0.03
7386	Wolfgang Bärer-Holt	1	1	3	0	0.03
7387	Xavier Baron	1	1	3	0	0.03
7388	Xiuying Feng	1	1	3	0	0.03
7389	Xiuying Zhao	1	1	3	0	0.03
7390	Yan Jiang	1	1	3	0	0.03
7391	Yan Lai	1	1	3	0	0.03
7392	Yan Yi	1	1	3	0	0.03
7393	Yang Qian	1	1	3	0	0.03
7394	Yang Xia	1	1	3	0	0.03
7395	Yashvi Bahri	1	1	3	0	0.03
7396	Yolanda Lee	1	1	3	0	0.03
7397	Yong Jiang	1	1	3	0	0.03
7398	Yong Xiao	1	1	3	0	0.03
7399	Yvette Cohen	1	1	3	0	0.03
7400	Yvonne Mullins	1	1	3	0	0.03
7401	Zachary Wells	1	1	3	0	0.03
7402	Zara Kibe	1	1	3	0	0.03
7403	Zbigniew Stey	1	1	3	0	0.03
7404	Frau Viola Ullmann	1	0	12	2	0.03
7405	Ivona Žáková	1	0	12	2	0.03
7406	Thies Seifert	1	0	12	2	0.03
7407	Cole Pierce	2	0	3	0	0.03
7408	Holden Rhys	2	0	3	0	0.03
7409	Max Everhart	2	0	3	0	0.03
7410	Abigail Scott	1	1	2	0	0.03
7411	Abram Kala	1	1	2	0	0.03
7412	Adam Fletcher	1	1	2	0	0.03
7413	Adam Kroker	1	1	2	0	0.03
7414	Adrianna Panasiewicz	1	1	2	0	0.03
7415	Advika Tailor	1	1	2	0	0.03
7416	Adélaïde Letellier de Cordier	1	1	2	0	0.03
7417	Agata Bähr-Metz	1	1	2	0	0.03
7418	Ahana Babu	1	1	2	0	0.03
7419	Alessandro Zito	1	1	2	0	0.03
7420	Alexa Adolph	1	1	2	0	0.03
7421	Alexis Hart	1	1	2	0	0.03
7422	Alf Bähr	1	1	2	0	0.03
7423	Alisha Fernandez	1	1	2	0	0.03
7424	Allison Williams	1	1	2	0	0.03
7425	Amanda Cole	1	1	2	0	0.03
7426	Anahi Buch	1	1	2	0	0.03
7427	Andrea White	1	1	2	0	0.03
7428	Andree Dippel	1	1	2	0	0.03
7429	Andree Tschentscher	1	1	2	0	0.03
7430	Andrew Carrillo	1	1	2	0	0.03
7431	Angel Mccullough	1	1	2	0	0.03
7432	Anna Woodard	1	1	2	0	0.03
7433	Annetta Fanucci	1	1	2	0	0.03
7434	Ashley Powers	1	1	2	0	0.03
7435	Astrid Weinhold	1	1	2	0	0.03
7436	Aurore Leroux	1	1	2	0	0.03
7437	Austin Adams	1	1	2	0	0.03
7438	Barbara Russell	1	1	2	0	0.03
7439	Belén de Trillo	1	1	2	0	0.03
7440	Betty Vega	1	1	2	0	0.03
7441	Bhamini Luthra	1	1	2	0	0.03
7442	Božena Dušková	1	1	2	0	0.03
7443	Bradley Reeves Jr.	1	1	2	0	0.03
7444	Brady Brown	1	1	2	0	0.03
7445	Brandon Horton	1	1	2	0	0.03
7446	Brandy Chung PhD	1	1	2	0	0.03
7447	Brett Bond	1	1	2	0	0.03
7448	Brett Whitehead	1	1	2	0	0.03
7449	Brian Melton	1	1	2	0	0.03
7450	Brian Mitchell	1	1	2	0	0.03
7451	Brian Moody	1	1	2	0	0.03
7452	Brian Price	1	1	2	0	0.03
7453	Brittany Horn	1	1	2	0	0.03
7454	Bruce Hernandez	1	1	2	0	0.03
7455	Bryan Hale	1	1	2	0	0.03
7456	Candace Anderson	1	1	2	0	0.03
7457	Capucine Martin	1	1	2	0	0.03
7458	Cas Everde	1	1	2	0	0.03
7459	Catalina Borja Barbero	1	1	2	0	0.03
7460	Catherine Mcdonald	1	1	2	0	0.03
7461	Cecilia Parejo Carrillo	1	1	2	0	0.03
7462	Cecilia Scholz	1	1	2	0	0.03
7463	Chantal Gomes	1	1	2	0	0.03
7464	Chantal-Geneviève Fournier	1	1	2	0	0.03
7465	Chao Mo	1	1	2	0	0.03
7466	Charles Jordan	1	1	2	0	0.03
7467	Charles Norman	1	1	2	0	0.03
7468	Charles Smith	1	1	2	0	0.03
7469	Charles Spears	1	1	2	0	0.03
7470	Charlotte Marchand	1	1	2	0	0.03
7471	Charvi Taneja	1	1	2	0	0.03
7472	Cheryl Wiley	1	1	2	0	0.03
7473	Chiyo Ota	1	1	2	0	0.03
7474	Christina Dalton	1	1	2	0	0.03
7475	Christine Mendoza	1	1	2	0	0.03
7476	Christopher Hardy	1	1	2	0	0.03
7477	Christopher Martin	1	1	2	0	0.03
7478	Claude Jean	1	1	2	0	0.03
7479	Cody Mathis	1	1	2	0	0.03
7480	Corey Ramirez	1	1	2	0	0.03
7481	Corinne Leleu	1	1	2	0	0.03
7482	Curt Wieloch	1	1	2	0	0.03
7483	Cynthia Hoover	1	1	2	0	0.03
7484	Cyprian Perczak	1	1	2	0	0.03
7485	Dale Bell	1	1	2	0	0.03
7486	Damini Wable	1	1	2	0	0.03
7487	Daniel Watson	1	1	2	0	0.03
7488	Danielle Moore	1	1	2	0	0.03
7489	Dariusz Madera	1	1	2	0	0.03
7490	Darshit Chopra	1	1	2	0	0.03
7491	David Gibson	1	1	2	0	0.03
7492	David Singleton	1	1	2	0	0.03
7493	David Sloan	1	1	2	0	0.03
7494	Dawn Kelly	1	1	2	0	0.03
7495	Debra Wolfe	1	1	2	0	0.03
7496	Derrick Scott	1	1	2	0	0.03
7497	Devansh Bakshi	1	1	2	0	0.03
7498	Divit Behl	1	1	2	0	0.03
7499	Donna Ellison	1	1	2	0	0.03
7500	Douglas Roberts	1	1	2	0	0.03
7501	Dustin Smith	1	1	2	0	0.03
7502	Edith Schaaf	1	1	2	0	0.03
7503	Edward Collier	1	1	2	0	0.03
7504	Edward Kirby	1	1	2	0	0.03
7505	Edward Kraushaar	1	1	2	0	0.03
7506	Elizabeth Mccann	1	1	2	0	0.03
7507	Elladio Badoer-Beccaria	1	1	2	0	0.03
7508	Emily Arnold	1	1	2	0	0.03
7509	Emily Werner	1	1	2	0	0.03
7510	Emin Mentzel	1	1	2	0	0.03
7511	Emma Ruiz	1	1	2	0	0.03
7512	Erica Shaffer	1	1	2	0	0.03
7513	Erika Nguyen	1	1	2	0	0.03
7514	Eshani Deep	1	1	2	0	0.03
7515	Eugene Powell	1	1	2	0	0.03
7516	Eugenie Scheel	1	1	2	0	0.03
7517	Evangelina Bautista Catalá	1	1	2	0	0.03
7518	Evi Dietz	1	1	2	0	0.03
7519	Fang Cai	1	1	2	0	0.03
7520	Farhan Seshadri	1	1	2	0	0.03
7521	Fenne Ferran	1	1	2	0	0.03
7522	Frank Green	1	1	2	0	0.03
7523	Frau Oxana Jüttner	1	1	2	0	0.03
7524	Frau Raphaela Bohnbach	1	1	2	0	0.03
7525	Friedrich Eberhardt	1	1	2	0	0.03
7526	Gaetano Hartmann	1	1	2	0	0.03
7527	George Lloyd	1	1	2	0	0.03
7528	Georges Delorme	1	1	2	0	0.03
7529	Gerald Shepherd	1	1	2	0	0.03
7530	Gina Lehmann-Süßebier	1	1	2	0	0.03
7531	Goffredo Cheda	1	1	2	0	0.03
7532	Graciana Sacristán Saura	1	1	2	0	0.03
7533	Gregory Morris	1	1	2	0	0.03
7534	Grzegorz Pacocha	1	1	2	0	0.03
7535	Gudrun Atzler	1	1	2	0	0.03
7536	Guiying Luo	1	1	2	0	0.03
7537	Hailey Andrews	1	1	2	0	0.03
7538	Hanne Kambs	1	1	2	0	0.03
7539	Hans-Jörg Lehmann	1	1	2	0	0.03
7540	Harriet Wilms-Segebahn	1	1	2	0	0.03
7541	Harro Gerlach	1	1	2	0	0.03
7542	Haruka Kobayashi	1	1	2	0	0.03
7543	Heather Hanna	1	1	2	0	0.03
7544	Heather Hardy	1	1	2	0	0.03
7545	Heidi Perez	1	1	2	0	0.03
7546	Heidi Smith	1	1	2	0	0.03
7547	Helene van der Dussen	1	1	2	0	0.03
7548	Henri Da Costa	1	1	2	0	0.03
7549	Henri Maillard	1	1	2	0	0.03
7550	Herr Heinz-Gerd Schleich	1	1	2	0	0.03
7551	Hilmar Budig-Seip	1	1	2	0	0.03
7552	Himmat Shroff	1	1	2	0	0.03
7553	Hiroshi Yamamoto	1	1	2	0	0.03
7554	Holger Trupp	1	1	2	0	0.03
7555	Honoré Leduc	1	1	2	0	0.03
7556	Honoré-Alexandre Vidal	1	1	2	0	0.03
7557	Howard Taylor	1	1	2	0	0.03
7558	Hunter Green	1	1	2	0	0.03
7559	Ian Santos	1	1	2	0	0.03
7560	Ildefonso Aragón Guerra	1	1	2	0	0.03
7561	Imelda Reising-Franke	1	1	2	0	0.03
7562	Isaac Huet du Cousin	1	1	2	0	0.03
7563	Isaiah Morales	1	1	2	0	0.03
7564	Jafet Páez-Montenegro	1	1	2	0	0.03
7565	James Beard	1	1	2	0	0.03
7566	James Ellis	1	1	2	0	0.03
7567	James George	1	1	2	0	0.03
7568	James Hopkins	1	1	2	0	0.03
7569	James Li	1	1	2	0	0.03
7570	James Odom	1	1	2	0	0.03
7571	Jamie Castro	1	1	2	0	0.03
7572	Janet Aguilar	1	1	2	0	0.03
7573	Janet Hunter	1	1	2	0	0.03
7574	Janin Hartmann	1	1	2	0	0.03
7575	Jason Ali	1	1	2	0	0.03
7576	Jason Robles	1	1	2	0	0.03
7577	Jason Vaughn	1	1	2	0	0.03
7578	Jeanette Carey	1	1	2	0	0.03
7579	Jeanette Wagner	1	1	2	0	0.03
7580	Jennifer Tate	1	1	2	0	0.03
7581	Jeremi Głuszko	1	1	2	0	0.03
7582	Jeremi Piórek	1	1	2	0	0.03
7583	Jessica Esparza	1	1	2	0	0.03
7584	Jhanvi Krishnamurthy	1	1	2	0	0.03
7585	Jie Hou	1	1	2	0	0.03
7586	Jie Peng	1	1	2	0	0.03
7587	Jie Qiu	1	1	2	0	0.03
7588	Jill Smith	1	1	2	0	0.03
7589	John Buchanan	1	1	2	0	0.03
7590	John Maldonado	1	1	2	0	0.03
7591	Jonathan Jones Jr.	1	1	2	0	0.03
7592	Jordan Hopkins	1	1	2	0	0.03
7593	Joseph Escobar	1	1	2	0	0.03
7594	Joseph Hart	1	1	2	0	0.03
7595	Joseph Whitaker	1	1	2	0	0.03
7596	Jules Lemonnier	1	1	2	0	0.03
7597	Julia Berry	1	1	2	0	0.03
7598	Julia Davis	1	1	2	0	0.03
7599	Julie Gonzalez	1	1	2	0	0.03
7600	Julie Griffin	1	1	2	0	0.03
7601	Julie Nielsen	1	1	2	0	0.03
7602	Julie Thomas	1	1	2	0	0.03
7603	Jun Zhong	1	1	2	0	0.03
7604	Kaitlyn Brown	1	1	2	0	0.03
7605	Kaitlyn Castaneda PhD	1	1	2	0	0.03
7606	Kamila Bobko	1	1	2	0	0.03
7607	Karl Bruder	1	1	2	0	0.03
7608	Karla Moreno	1	1	2	0	0.03
7609	Katelyn Malone	1	1	2	0	0.03
7610	Katherine Tanner	1	1	2	0	0.03
7611	Kathleen Fowler	1	1	2	0	0.03
7612	Katy Spieß	1	1	2	0	0.03
7613	Kayla Martinez	1	1	2	0	0.03
7614	Kelly Barrett	1	1	2	0	0.03
7615	Kelly Clayton	1	1	2	0	0.03
7616	Kenneth Hall	1	1	2	0	0.03
7617	Kenneth Lopez	1	1	2	0	0.03
7618	Kevin Thomas	1	1	2	0	0.03
7619	Kimberly Porter	1	1	2	0	0.03
7620	Klaus-Werner Löchel	1	1	2	0	0.03
7621	Kristin Shepard	1	1	2	0	0.03
7622	Krystal Reese	1	1	2	0	0.03
7623	Kyle Lawrence	1	1	2	0	0.03
7624	Larry Bailey	1	1	2	0	0.03
7625	Laura Carson	1	1	2	0	0.03
7626	Laurie Anderson	1	1	2	0	0.03
7627	Lei Chang	1	1	2	0	0.03
7628	Lei Dong	1	1	2	0	0.03
7629	Lei Xu	1	1	2	0	0.03
7630	Li Cao	1	1	2	0	0.03
7631	Li Su	1	1	2	0	0.03
7632	Lidia Dörschner-Käster	1	1	2	0	0.03
7633	Linda Bailey	1	1	2	0	0.03
7634	Liwia Mitera	1	1	2	0	0.03
7635	Loredana Bertolucci	1	1	2	0	0.03
7636	Loretta Börner	1	1	2	0	0.03
7637	Lori Montgomery	1	1	2	0	0.03
7638	Léon Lemaire	1	1	2	0	0.03
7639	Maaya Hayashi	1	1	2	0	0.03
7640	Maaya Suzuki	1	1	2	0	0.03
7641	Madeline Gentry	1	1	2	0	0.03
7642	Manjari Varty	1	1	2	0	0.03
7643	Manuel Morrison	1	1	2	0	0.03
7644	Mar Abril Balaguer	1	1	2	0	0.03
7645	Margaret Mueller	1	1	2	0	0.03
7646	Margot Adam	1	1	2	0	0.03
7647	Marika Rörricht	1	1	2	0	0.03
7648	Marine Goncalves	1	1	2	0	0.03
7649	Martha Patrick	1	1	2	0	0.03
7650	Martin Anderson	1	1	2	0	0.03
7651	Martino Federico	1	1	2	0	0.03
7652	Mary Morales	1	1	2	0	0.03
7653	Mathilde Texier	1	1	2	0	0.03
7654	Matthew Davidson	1	1	2	0	0.03
7655	Matthew Phillips	1	1	2	0	0.03
7656	Maurycy Seidel	1	1	2	0	0.03
7657	Meghan Cortez	1	1	2	0	0.03
7658	Melanie Heß	1	1	2	0	0.03
7659	Michael Cole	1	1	2	0	0.03
7660	Michael Grant	1	1	2	0	0.03
7661	Michael Jackson	1	1	2	0	0.03
7662	Michael Roberts	1	1	2	0	0.03
7663	Michael Snyder	1	1	2	0	0.03
7664	Michael White	1	1	2	0	0.03
7665	Michaela Brooks	1	1	2	0	0.03
7666	Michelle Castro	1	1	2	0	0.03
7667	Michelle Gilbert	1	1	2	0	0.03
7668	Michelle Herrera	1	1	2	0	0.03
7669	Michelle Russo	1	1	2	0	0.03
7670	Michelle Shields	1	1	2	0	0.03
7671	Mikako Saito	1	1	2	0	0.03
7672	Min Du	1	1	2	0	0.03
7673	Min Kang	1	1	2	0	0.03
7674	Ming Meng	1	1	2	0	0.03
7675	Miss Erin Dean	1	1	2	0	0.03
7676	Mitchell Mcdonald	1	1	2	0	0.03
7677	Monica Coleman	1	1	2	0	0.03
7678	Monica Hunt	1	1	2	0	0.03
7679	Monica Mason	1	1	2	0	0.03
7680	Monika Leonowicz	1	1	2	0	0.03
7681	Na Huang	1	1	2	0	0.03
7682	Nancy Franklin	1	1	2	0	0.03
7683	Nancy Myers	1	1	2	0	0.03
7684	Naoko Suzuki	1	1	2	0	0.03
7685	Narrer på Vagt	1	1	2	0	0.03
7686	Natan Molka	1	1	2	0	0.03
7687	Navya Jhaveri	1	1	2	0	0.03
7688	Navya Kala	1	1	2	0	0.03
7689	Nicholas Rasmussen	1	1	2	0	0.03
7690	Nicolas Rodriguez	1	1	2	0	0.03
7691	Nicole Valenzuela	1	1	2	0	0.03
7692	Nino Pometta	1	1	2	0	0.03
7693	Olivia Jarvis	1	1	2	0	0.03
7694	Onofre Chacón Jordá	1	1	2	0	0.03
7695	Ottilie Fiebig	1	1	2	0	0.03
7696	Paco Yáñez Bravo	1	1	2	0	0.03
7697	Parinaaz Krishnamurthy	1	1	2	0	0.03
7698	Patricia Cherry	1	1	2	0	0.03
7699	Patrick Herrera	1	1	2	0	0.03
7700	Patrizia Drewes	1	1	2	0	0.03
7701	Paul Kirk	1	1	2	0	0.03
7702	Philippine du Pottier	1	1	2	0	0.03
7703	Pierre David	1	1	2	0	0.03
7704	Ping Fang	1	1	2	0	0.03
7705	Rafał Helak	1	1	2	0	0.03
7706	Raymond Blackburn	1	1	2	0	0.03
7707	Raymond Gates	1	1	2	0	0.03
7708	Raymond Martin	1	1	2	0	0.03
7709	Rebecca Bishop	1	1	2	0	0.03
7710	Rebecca Byrd	1	1	2	0	0.03
7711	Recep Schüler	1	1	2	0	0.03
7712	Regina Vance	1	1	2	0	0.03
7713	Renee Carr	1	1	2	0	0.03
7714	Reyansh Barad	1	1	2	0	0.03
7715	Rhea Chatterjee	1	1	2	0	0.03
7716	Rhea Dhar	1	1	2	0	0.03
7717	Rhonda Guerrero	1	1	2	0	0.03
7718	Richard Kaiser	1	1	2	0	0.03
7719	Richard Mckenzie	1	1	2	0	0.03
7720	Richard Vazquez	1	1	2	0	0.03
7721	Richard Ward	1	1	2	0	0.03
7722	Ritvik Venkatesh	1	1	2	0	0.03
7723	Robert Fernandez	1	1	2	0	0.03
7724	Robert Fowler	1	1	2	0	0.03
7725	Robert Gonzales	1	1	2	0	0.03
7726	Robert Nelson	1	1	2	0	0.03
7727	Robert Renault	1	1	2	0	0.03
7728	Rodney Hayes	1	1	2	0	0.03
7729	Roksana Kurzak	1	1	2	0	0.03
7730	Rolando Paruta	1	1	2	0	0.03
7731	Ronald Esparza	1	1	2	0	0.03
7732	Rosa-Maria Beyer	1	1	2	0	0.03
7733	Rosenda Reyes Salazar	1	1	2	0	0.03
7734	Ryan Hamilton	1	1	2	0	0.03
7735	Ryan Richards	1	1	2	0	0.03
7736	Ryan Smith	1	1	2	0	0.03
7737	Ryohei Nakajima	1	1	2	0	0.03
7738	Ryosuke Nakamura	1	1	2	0	0.03
7739	Rüdiger Schinke	1	1	2	0	0.03
7740	Saira Bose	1	1	2	0	0.03
7741	Samantha Taylor	1	1	2	0	0.03
7742	Samiha Hayre	1	1	2	0	0.03
7743	Samuel Lewis	1	1	2	0	0.03
7744	Sandra Riley	1	1	2	0	0.03
7745	Sara Seguí Vallés	1	1	2	0	0.03
7746	Sara Sharaf	1	1	2	0	0.03
7747	Scott Gutierrez	1	1	2	0	0.03
7748	Shane Clark	1	1	2	0	0.03
7749	Shannon Castro	1	1	2	0	0.03
7750	Sharon Roberts	1	1	2	0	0.03
7751	Shawn Hart	1	1	2	0	0.03
7752	Shohei Yamashita	1	1	2	0	0.03
7753	Shota Mori	1	1	2	0	0.03
7754	Stephanie Benjamin	1	1	2	0	0.03
7755	Stephanie Brown	1	1	2	0	0.03
7756	Stephanie Hanson	1	1	2	0	0.03
7757	Stephen Chavez	1	1	2	0	0.03
7758	Steve Schottin	1	1	2	0	0.03
7759	Steven Williams	1	1	2	0	0.03
7760	Summer Bender	1	1	2	0	0.03
7761	Suzanne Ramos	1	1	2	0	0.03
7762	Sven Oude Heer	1	1	2	0	0.03
7763	Takuma Shimizu	1	1	2	0	0.03
7764	Tammy Crane	1	1	2	0	0.03
7765	Tao Fan	1	1	2	0	0.03
7766	Tara Becker	1	1	2	0	0.03
7767	Tara Kaul	1	1	2	0	0.03
7768	Taylor Poole	1	1	2	0	0.03
7769	Terry Baker	1	1	2	0	0.03
7770	Theres Roht	1	1	2	0	0.03
7771	Thomas Boyle	1	1	2	0	0.03
7772	Thomas Duncan	1	1	2	0	0.03
7773	Thomas Mccoy	1	1	2	0	0.03
7774	Thomas Ramirez	1	1	2	0	0.03
7775	Tiffany Wong	1	1	2	0	0.03
7776	Timothy Chen	1	1	2	0	0.03
7777	Timothy Haley	1	1	2	0	0.03
7778	Timothy Kelly	1	1	2	0	0.03
7779	Timothée Lefèvre	1	1	2	0	0.03
7780	Tobiasz Kargol	1	1	2	0	0.03
7781	Tom Kade	1	1	2	0	0.03
7782	Tracy Combs	1	1	2	0	0.03
7783	Tracy Vazquez	1	1	2	0	0.03
7784	Traude Salz	1	1	2	0	0.03
7785	Traudl Jacob	1	1	2	0	0.03
7786	Travis Schmitt	1	1	2	0	0.03
7787	Trevor Gonzales	1	1	2	0	0.03
7788	Tyler Smith	1	1	2	0	0.03
7789	Tülay Tlustek	1	1	2	0	0.03
7790	Valentine Jourdan du Schneider	1	1	2	0	0.03
7791	Valerio Zapata-Rico	1	1	2	0	0.03
7792	Vedika Dar	1	1	2	0	0.03
7793	Veer Raju	1	1	2	0	0.03
7794	Vidur Khatri	1	1	2	0	0.03
7795	William Bates	1	1	2	0	0.03
7796	William Kennedy	1	1	2	0	0.03
7797	William Warren	1	1	2	0	0.03
7798	Wojciech Ratka	1	1	2	0	0.03
7799	Xia Tao	1	1	2	0	0.03
7800	Xia Yan	1	1	2	0	0.03
7801	Xiulan Chen	1	1	2	0	0.03
7802	Xiuying Yi	1	1	2	0	0.03
7803	Yan Mo	1	1	2	0	0.03
7804	Yang Fu	1	1	2	0	0.03
7805	Yang Shi	1	1	2	0	0.03
7806	Yolanda Mendoza	1	1	2	0	0.03
7807	Yolanda Roy	1	1	2	0	0.03
7808	Yong Zhao	1	1	2	0	0.03
7809	Zain Datta	1	1	2	0	0.03
7810	Élise Moreno	1	1	2	0	0.03
7811	Anthony Clark	1	0	15	1	0.03
7812	Deborah Matthews	1	0	15	1	0.03
7813	Douglas Haynes	1	0	15	1	0.03
7814	Jochen Becker	1	0	15	1	0.03
7815	John Mitchell	1	0	15	1	0.03
7816	Rhonda Ward	1	0	15	1	0.03
7817	Stilla Seifert	1	0	15	1	0.03
7818	Tilly Eigenwillig	1	0	15	1	0.03
7819	Timothy Simpson	1	0	15	1	0.03
7820	Aaron Jones	1	1	1	0	0.03
7821	Abraham Becker	1	1	1	0	0.03
7822	Agnes Stahr	1	1	1	0	0.03
7823	Ahana Sarin	1	1	1	0	0.03
7824	Akemi Nishimura	1	1	1	0	0.03
7825	Akemi Suzuki	1	1	1	0	0.03
7826	Alan Castro	1	1	1	0	0.03
7827	Alan Smith	1	1	1	0	0.03
7828	Alan Yang	1	1	1	0	0.03
7829	Albert White	1	1	1	0	0.03
7830	Albrecht Scholtz	1	1	1	0	0.03
7831	Alejandro Brown	1	1	1	0	0.03
7832	Alexis Church	1	1	1	0	0.03
7833	Alphonse Berthelot	1	1	1	0	0.03
7834	Amanda Mcdonald	1	1	1	0	0.03
7835	Amelie Roht	1	1	1	0	0.03
7836	Ana Garrison	1	1	1	0	0.03
7837	Ana Wilson	1	1	1	0	0.03
7838	Andrea Ramirez	1	1	1	0	0.03
7839	Angelica Ruiz	1	1	1	0	0.03
7840	Anthony Larson	1	1	1	0	0.03
7841	Autumn Knight	1	1	1	0	0.03
7842	Baccio Caracciolo-Pelli	1	1	1	0	0.03
7843	Beata Budig	1	1	1	0	0.03
7844	Bianca Pugliese	1	1	1	0	0.03
7845	Brandon Waller	1	1	1	0	0.03
7846	Carlos Franco	1	1	1	0	0.03
7847	Carrie Morris	1	1	1	0	0.03
7848	Catherine Parker	1	1	1	0	0.03
7849	Charles Normand	1	1	1	0	0.03
7850	Charles Shaw	1	1	1	0	0.03
7851	Chelsea Mata	1	1	1	0	0.03
7852	Chiyo Nakajima	1	1	1	0	0.03
7853	Christopher Brown	1	1	1	0	0.03
7854	Christopher Wells	1	1	1	0	0.03
7855	Christy Hicks	1	1	1	0	0.03
7856	Concha Roselló Verdugo	1	1	1	0	0.03
7857	Cynthia Johnson	1	1	1	0	0.03
7858	Cynthia Pierce	1	1	1	0	0.03
7859	Cyprian Goss	1	1	1	0	0.03
7860	DRIP$ETTA	1	1	1	0	0.03
7861	Darshit Gandhi	1	1	1	0	0.03
7862	Darshit Ratta	1	1	1	0	0.03
7863	David Campbell	1	1	1	0	0.03
7864	Deborah Cruz	1	1	1	0	0.03
7865	Deborah Young	1	1	1	0	0.03
7866	Debra Jones	1	1	1	0	0.03
7867	Devansh De	1	1	1	0	0.03
7868	Dhanuk Mane	1	1	1	0	0.03
7869	Diana Wilkinson	1	1	1	0	0.03
7870	Divyansh Sehgal	1	1	1	0	0.03
7871	Dustin Maxwell	1	1	1	0	0.03
7872	Elizabeth Montgomery	1	1	1	0	0.03
7873	Emily Hudson	1	1	1	0	0.03
7874	Enzio Caffarelli	1	1	1	0	0.03
7875	Erin Martinez	1	1	1	0	0.03
7876	Ethan Wiley	1	1	1	0	0.03
7877	Eulalia Patiño Ballester	1	1	1	0	0.03
7878	Evie Feenstra	1	1	1	0	0.03
7879	Fateh Bhatt	1	1	1	0	0.03
7880	Folkert Schmidtke	1	1	1	0	0.03
7881	Francisco Säuberlich	1	1	1	0	0.03
7882	Franck du Mercier	1	1	1	0	0.03
7883	Gabrielle Brady	1	1	1	0	0.03
7884	Gaja Powierża	1	1	1	0	0.03
7885	Gary Wilson	1	1	1	0	0.03
7886	George Bailey	1	1	1	0	0.03
7887	Gianmarco Verga	1	1	1	0	0.03
7888	Giovanna Bongiorno	1	1	1	0	0.03
7889	Giulietta Parini	1	1	1	0	0.03
7890	Grant Bruce	1	1	1	0	0.03
7891	Gregory Evans	1	1	1	0	0.03
7892	Guiying Mao	1	1	1	0	0.03
7893	Hannah Richard	1	1	1	0	0.03
7894	Hans-Joachim Seip	1	1	1	0	0.03
7895	Heather Davis	1	1	1	0	0.03
7896	Heinz-Georg Bolzmann	1	1	1	0	0.03
7897	Hilma Wirth	1	1	1	0	0.03
7898	Hortense Peron	1	1	1	0	0.03
7899	Hrishita Deep	1	1	1	0	0.03
7900	Hunar Dugal	1	1	1	0	0.03
7901	Imke Hartmann	1	1	1	0	0.03
7902	Indrajit Chana	1	1	1	0	0.03
7903	Ira Gole	1	1	1	0	0.03
7904	Ishaan Bal	1	1	1	0	0.03
7905	Isla Quinn	1	1	1	0	0.03
7906	Ivana Dhillon	1	1	1	0	0.03
7907	Jacek Kazimierczuk	1	1	1	0	0.03
7908	Jacob Bryant	1	1	1	0	0.03
7909	Jacob Gallagher	1	1	1	0	0.03
7910	James Gibbs	1	1	1	0	0.03
7911	James Harvey	1	1	1	0	0.03
7912	Janet Powers	1	1	1	0	0.03
7913	Janette Reuter	1	1	1	0	0.03
7914	Janice Villarreal	1	1	1	0	0.03
7915	Janna Holt	1	1	1	0	0.03
7916	Jason Mcpherson	1	1	1	0	0.03
7917	Jason Owens	1	1	1	0	0.03
7918	Jayan Barman	1	1	1	0	0.03
7919	Jeff Lewis	1	1	1	0	0.03
7920	Jeffrey Garrett	1	1	1	0	0.03
7921	Jeffrey Morgan	1	1	1	0	0.03
7922	Jenna Casey	1	1	1	0	0.03
7923	Jennifer Coleman	1	1	1	0	0.03
7924	Jennifer Ferguson	1	1	1	0	0.03
7925	Jessica Carney	1	1	1	0	0.03
7926	Jessica Garcia	1	1	1	0	0.03
7927	Jie Guo	1	1	1	0	0.03
7928	Jimmy Brown	1	1	1	0	0.03
7929	Jivin Wali	1	1	1	0	0.03
7930	Jodi Salazar	1	1	1	0	0.03
7931	Joe Mccarthy	1	1	1	0	0.03
7932	Jonathan Bishop	1	1	1	0	0.03
7933	Jonathan Thompson	1	1	1	0	0.03
7934	Jorge Atkinson	1	1	1	0	0.03
7935	Josep Nicanor Salcedo Arroyo	1	1	1	0	0.03
7936	Joseph Adams	1	1	1	0	0.03
7937	Joseph Contreras	1	1	1	0	0.03
7938	Joseph Wright	1	1	1	0	0.03
7939	Joshua Morrison	1	1	1	0	0.03
7940	Juan Qin	1	1	1	0	0.03
7941	Juan Yi	1	1	1	0	0.03
7942	Julianna Mytnik	1	1	1	0	0.03
7943	Julie Flores	1	1	1	0	0.03
7944	Julita Łaciak	1	1	1	0	0.03
7945	Juliusz Cierpisz	1	1	1	0	0.03
7946	Juliusz Hawrylak	1	1	1	0	0.03
7947	Kabir Dutta	1	1	1	0	0.03
7948	Karol Szczechowicz	1	1	1	0	0.03
7949	Kelly Briggs	1	1	1	0	0.03
7950	Kenichi Suzuki	1	1	1	0	0.03
7951	Kenneth Guerra	1	1	1	0	0.03
7952	Kenneth Ruiz	1	1	1	0	0.03
7953	Kevin Ayala	1	1	1	0	0.03
7954	Kevin Gilbert	1	1	1	0	0.03
7955	Kim Craig	1	1	1	0	0.03
7956	Kimaya Chaudhuri	1	1	1	0	0.03
7957	Kimberly Jones	1	1	1	0	0.03
7958	Kumiko Sato	1	1	1	0	0.03
7959	Kyle Cook	1	1	1	0	0.03
7960	Kyle Jackson	1	1	1	0	0.03
7961	Leroy Reeves	1	1	1	0	0.03
7962	Li Wang	1	1	1	0	0.03
7963	Lina Rensi	1	1	1	0	0.03
7964	Lisa Johnson	1	1	1	0	0.03
7965	Lori Schmidt	1	1	1	0	0.03
7966	Luc van Poppel-Hulskes	1	1	1	0	0.03
7967	Lucas Lenoir du Colin	1	1	1	0	0.03
7968	Maks Bodek	1	1	1	0	0.03
7969	Maks Grębowiec	1	1	1	0	0.03
7970	Marcin Jeżyk	1	1	1	0	0.03
7971	Marion Hellwig-Berger	1	1	1	0	0.03
7972	Mark Singleton	1	1	1	0	0.03
7973	Mary Frazier	1	1	1	0	0.03
7974	Mary Gordon	1	1	1	0	0.03
7975	Mary Mies	1	1	1	0	0.03
7976	Mary Williams	1	1	1	0	0.03
7977	Matthew Tran	1	1	1	0	0.03
7978	Max Brown	1	1	1	0	0.03
7979	Mckenzie Cuevas	1	1	1	0	0.03
7980	Mehul Varty	1	1	1	0	0.03
7981	Melanie Browning PhD	1	1	1	0	0.03
7982	Melissa Watson	1	1	1	0	0.03
7983	Michael Farrell	1	1	1	0	0.03
7984	Michael Lin	1	1	1	0	0.03
7985	Michelle Montoya	1	1	1	0	0.03
7986	Miluše Malá	1	1	1	0	0.03
7987	Min Xiao	1	1	1	0	0.03
7988	Na Ding	1	1	1	0	0.03
7989	Na Tao	1	1	1	0	0.03
7990	Natasha Wood	1	1	1	0	0.03
7991	Nathan Hernandez	1	1	1	0	0.03
7992	Nathaniel Fisher	1	1	1	0	0.03
7993	Nicholas Pope	1	1	1	0	0.03
7994	Nicolai Caspar	1	1	1	0	0.03
7995	Nino Canova-Esposito	1	1	1	0	0.03
7996	Ottomar Spieß	1	1	1	0	0.03
7997	Pamela Moran	1	1	1	0	0.03
7998	Pamela Schultz	1	1	1	0	0.03
7999	Pari Sharaf	1	1	1	0	0.03
8000	Paul Ashley	1	1	1	0	0.03
8001	Paul Contreras	1	1	1	0	0.03
8002	Phillip Dennis	1	1	1	0	0.03
8003	Ping Zhao	1	1	1	0	0.03
8004	Piya Ravel	1	1	1	0	0.03
8005	Qiang Hou	1	1	1	0	0.03
8006	Rachel Lewis	1	1	1	0	0.03
8007	Richard Mathieu de Perrin	1	1	1	0	0.03
8008	Ritvik Taneja	1	1	1	0	0.03
8009	Riya Dhingra	1	1	1	0	0.03
8010	Robert Bass	1	1	1	0	0.03
8011	Robert Strickland	1	1	1	0	0.03
8012	Ronald Barnett	1	1	1	0	0.03
8013	Rosa Flaiano	1	1	1	0	0.03
8014	Ruben Green	1	1	1	0	0.03
8015	Ryan Howard	1	1	1	0	0.03
8016	Ryan King	1	1	1	0	0.03
8017	Ryan Murphy	1	1	1	0	0.03
8018	Sabrina Hughes	1	1	1	0	0.03
8019	Samaira Borah	1	1	1	0	0.03
8020	Samarth Saraf	1	1	1	0	0.03
8021	Sandra Horton	1	1	1	0	0.03
8022	Sandra Reyes	1	1	1	0	0.03
8023	Sara Levy	1	1	1	0	0.03
8024	Sarah Anderson	1	1	1	0	0.03
8025	Scott Kemp	1	1	1	0	0.03
8026	Shannon Schaefer	1	1	1	0	0.03
8027	Shawn Saunders	1	1	1	0	0.03
8028	Shelia Miller	1	1	1	0	0.03
8029	Shlok Cherian	1	1	1	0	0.03
8030	Shota Ito	1	1	1	0	0.03
8031	Simone Maurice	1	1	1	0	0.03
8032	Stacey Perry	1	1	1	0	0.03
8033	Stacy Davenport	1	1	1	0	0.03
8034	Stephanie Potter	1	1	1	0	0.03
8035	Susan Romero	1	1	1	0	0.03
8036	Sylwia Hołota	1	1	1	0	0.03
8037	Tanya Garrett	1	1	1	0	0.03
8038	Tara Dennis	1	1	1	0	0.03
8039	Tara Deshmukh	1	1	1	0	0.03
8040	Tara Ghose	1	1	1	0	0.03
8041	Tara Rivera	1	1	1	0	0.03
8042	Teófilo Flor Gras	1	1	1	0	0.03
8043	The Lunar Manifesto	1	1	1	0	0.03
8044	Thomas Carson	1	1	1	0	0.03
8045	Thomas Jones	1	1	1	0	0.03
8046	Tiffany Fitzpatrick	1	1	1	0	0.03
8047	Tiya Desai	1	1	1	0	0.03
8048	Tricia Yoder	1	1	1	0	0.03
8049	Tsubasa Sasaki	1	1	1	0	0.03
8050	Umang Bala	1	1	1	0	0.03
8051	Valentina Geraci	1	1	1	0	0.03
8052	Vincent Berg	1	1	1	0	0.03
8053	Vivaan Kala	1	1	1	0	0.03
8054	Vlastimil Bílek	1	1	1	0	0.03
8055	Wei Jiang	1	1	1	0	0.03
8056	William Fritz	1	1	1	0	0.03
8057	William Garner	1	1	1	0	0.03
8058	William Navarro	1	1	1	0	0.03
8059	William Ruiz	1	1	1	0	0.03
8060	William Tran	1	1	1	0	0.03
8061	Xia Huang	1	1	1	0	0.03
8062	Xiulan Xiao	1	1	1	0	0.03
8063	Yan Zeng	1	1	1	0	0.03
8064	Yan Zhang	1	1	1	0	0.03
8065	Yang Gong	1	1	1	0	0.03
8066	Yasmin Vig	1	1	1	0	0.03
8067	Yasuhiro Nakamura	1	1	1	0	0.03
8068	Yong Wan	1	1	1	0	0.03
8069	Emmanuelle Leleu	1	0	14	1	0.03
8070	Gilles Peltier-Rocher	1	0	14	1	0.03
8071	Adrian Bridges	1	0	10	2	0.03
8072	Annalisa Saragat-Pininfarina	1	0	10	2	0.03
8073	Christian Scott	1	0	10	2	0.03
8074	Lolita Speri	1	0	10	2	0.03
8075	Margaret Brun	1	0	10	2	0.03
8076	Vanessa Leon	1	0	10	2	0.03
8077	Alex Steele	1	1	0	0	0.03
8078	Amanda Powell	1	1	0	0	0.03
8079	Andrea Green	1	1	0	0	0.03
8080	Anna Reyes	1	1	0	0	0.03
8081	Caroline Lloyd	1	1	0	0	0.03
8082	Christopher Glover	1	1	0	0	0.03
8083	Christopher Ward	1	1	0	0	0.03
8084	Filippa Piane	1	1	0	0	0.03
8085	Frederik Hartung	1	1	0	0	0.03
8086	Heidi Berry	1	1	0	0	0.03
8087	Hridaan Rout	1	1	0	0	0.03
8088	James Goodwin	1	1	0	0	0.03
8089	Jeffery Reese	1	1	0	0	0.03
8090	Jeffrey Middleton	1	1	0	0	0.03
8091	Jessica Russell	1	1	0	0	0.03
8092	Joan Taylor	1	1	0	0	0.03
8093	John Baxter	1	1	0	0	0.03
8094	Julie Larson	1	1	0	0	0.03
8095	Katherine Davis	1	1	0	0	0.03
8096	Katherine Stevens	1	1	0	0	0.03
8097	Klara Bisaga	1	1	0	0	0.03
8098	Konrad Koźlak	1	1	0	0	0.03
8099	Laura Cohen	1	1	0	0	0.03
8100	Lisa Maxwell	1	1	0	0	0.03
8101	Marit Thies	1	1	0	0	0.03
8102	Matthew Sanchez	1	1	0	0	0.03
8103	Michał Flasza	1	1	0	0	0.03
8104	Nanami Sakamoto	1	1	0	0	0.03
8105	Nicholas Johnson	1	1	0	0	0.03
8106	Radosław Krzyk	1	1	0	0	0.03
8107	Susan Jones	1	1	0	0	0.03
8108	Svetlana Freudenberger	1	1	0	0	0.03
8109	Tadeusz Kieszek	1	1	0	0	0.03
8110	Terry Olson	1	1	0	0	0.03
8111	Vidur Dasgupta	1	1	0	0	0.03
8112	Herr Heinfried Kraushaar	1	0	9	2	0.03
8113	Alison Nelson	1	0	8	2	0.03
8114	Eric Gomez	1	0	8	2	0.03
8115	Kimberly Stevens	1	0	8	2	0.03
8116	Xiuying Chen	1	0	8	2	0.03
8117	Bobby Hester	1	0	11	1	0.02
8118	Brian Holmes	1	0	11	1	0.02
8119	Jan Jaśko	1	0	11	1	0.02
8120	Konrad Szpyt	1	0	11	1	0.02
8121	Shari Gomez	1	0	11	1	0.02
8122	Freddy Dörr	1	0	7	2	0.02
8123	Gina Lehmann-Nohlmans	1	0	7	2	0.02
8124	Hans-Ludwig Käster	1	0	7	2	0.02
8125	Niels Hering	1	0	7	2	0.02
8126	Anni Bauer	1	0	10	1	0.02
8127	Cornelius Kabus	1	0	10	1	0.02
8128	Friedo Eberhardt	1	0	10	1	0.02
8129	Heinz-Josef Ritter	1	0	10	1	0.02
8130	Sepp Köhler	1	0	10	1	0.02
8131	Vadim Killer	1	0	10	1	0.02
8132	Amanda Davidson	1	0	13	0	0.02
8133	Barbara Hall	1	0	13	0	0.02
8134	Cassandra Norris	1	0	13	0	0.02
8135	Danielle Peters	1	0	13	0	0.02
8136	Katherine Castillo	1	0	13	0	0.02
8137	Kelli Rojas	1	0	13	0	0.02
8138	Mary Gonzalez	1	0	13	0	0.02
8139	Rebecca Taylor	1	0	13	0	0.02
8140	Tonya Andrews	1	0	13	0	0.02
8141	Casey Green	1	0	9	1	0.02
8142	Frau Sybille Becker	1	0	9	1	0.02
8143	Fritz Schuster	1	0	9	1	0.02
8144	John Goodwin	1	0	9	1	0.02
8145	Miranda Page	1	0	9	1	0.02
8146	Patricia Rodriguez	1	0	9	1	0.02
8147	Radmila Stey-Kambs	1	0	9	1	0.02
8148	Rick House	1	0	9	1	0.02
8149	Ronald Rice	1	0	9	1	0.02
8150	Susan Rogers	1	0	9	1	0.02
8151	Aarav Chada	1	0	8	1	0.02
8152	Jennifer Alexander	1	0	8	1	0.02
8153	Kristina Lang	1	0	8	1	0.02
8154	Lauren Steele	1	0	8	1	0.02
8155	Lori Young	1	0	8	1	0.02
8156	Sean Riggs	1	0	8	1	0.02
8157	Tiffany Cain	1	0	8	1	0.02
8158	Camilla Sontag	1	0	7	1	0.02
8159	Irmtrud Schmiedecke	1	0	7	1	0.02
8160	Rosalie Bonbach	1	0	7	1	0.02
8161	Shane Davis	1	0	7	1	0.02
8162	Xia Dong	1	0	7	1	0.02
8163	Adrien Gonzalez	1	0	6	1	0.02
8164	Anastasie Auger	1	0	6	1	0.02
8165	Georges-Grégoire Martin	1	0	6	1	0.02
8166	Bronze Tiger	1	0	5	1	0.01
8167	Bryce Webb	1	0	5	1	0.01
8168	Debra Richards	1	0	5	1	0.01
8169	Lisa Frost	1	0	5	1	0.01
8170	Michael Hall	1	0	5	1	0.01
8171	Andrew Oneill	1	0	8	0	0.01
8172	Beth Walters	1	0	8	0	0.01
8173	Jacob Wiek	1	0	8	0	0.01
8174	Jennifer Ruiz	1	0	8	0	0.01
8175	Nina Anders	1	0	8	0	0.01
8176	Rachel Luna	1	0	8	0	0.01
8177	Regina Watkins	1	0	8	0	0.01
8178	Sandra Holden	1	0	8	0	0.01
8179	Tabitha Jones	1	0	8	0	0.01
8180	Tyler Austin	1	0	8	0	0.01
8181	Alex Hernandez	1	0	4	1	0.01
8182	Cesar Nguyen	1	0	4	1	0.01
8183	Elizabeth Donaldson	1	0	4	1	0.01
8184	Kevin Russell	1	0	4	1	0.01
8185	Stanley Perez	1	0	4	1	0.01
8186	Sydney Mack	1	0	4	1	0.01
8187	Kristina Jones	1	0	7	0	0.01
8188	Claudia Cunningham	1	0	3	1	0.01
8189	James Palmer	1	0	3	1	0.01
8190	Janina Gronkiewicz	1	0	3	1	0.01
8191	Phillip Barnes	1	0	3	1	0.01
8192	Robert Hughes	1	0	3	1	0.01
8193	Tao Xue	1	0	6	0	0.01
8194	Jennifer Moore	1	0	2	1	0.01
8195	Benjamin Gutierrez	1	0	5	0	0.01
8196	Bradley Valencia	1	0	5	0	0.01
8197	Brittany Sanchez	1	0	5	0	0.01
8198	Charlene Collins	1	0	5	0	0.01
8199	Elizabeth Brennan	1	0	5	0	0.01
8200	Jason Davis	1	0	5	0	0.01
8201	Lisa Sparks	1	0	5	0	0.01
8202	Maria Rubio	1	0	5	0	0.01
8203	Patrick Wall	1	0	5	0	0.01
8204	Timothy Sanchez	1	0	5	0	0.01
8205	Andean Horizon	1	0	4	0	0.01
8206	B.D. Wolf	1	0	4	0	0.01
8207	Connor Juarez	1	0	4	0	0.01
8208	Darren Pineda	1	0	4	0	0.01
8209	Dishani Sundaram	1	0	4	0	0.01
8210	Doris Klemt-Junck	1	0	4	0	0.01
8211	Eon	1	0	4	0	0.01
8212	Gokul Kapur	1	0	4	0	0.01
8213	Indrans Vyas	1	0	4	0	0.01
8214	Isabella Johnson	1	0	4	0	0.01
8215	James Weber	1	0	4	0	0.01
8216	Jing Bai	1	0	4	0	0.01
8217	Lei Hao	1	0	4	0	0.01
8218	Lucas Smith	1	0	4	0	0.01
8219	Mark Dunlap	1	0	4	0	0.01
8220	Melissa Douglas	1	0	4	0	0.01
8221	Philippine Colin	1	0	4	0	0.01
8222	Riaan Kalla	1	0	4	0	0.01
8223	Saira Kala	1	0	4	0	0.01
8224	Sean Cameron	1	0	4	0	0.01
8225	Shota Suzuki	1	0	4	0	0.01
8226	The Hollow Vestige	1	0	4	0	0.01
8227	William Smith	1	0	4	0	0.01
8228	Elizabeth Leon	1	0	3	0	0.01
8229	Gregory Kane	1	0	3	0	0.01
8230	Günther Mude	1	0	3	0	0.01
8231	Jessica Coleman	1	0	3	0	0.01
8232	Joerg Weiß	1	0	3	0	0.01
8233	Kim Bishop	1	0	3	0	0.01
8234	Manuel Roberts	1	0	3	0	0.01
8235	Megan Stephenson	1	0	3	0	0.01
8236	Michele Orozco	1	0	3	0	0.01
8237	Molly Whitaker	1	0	3	0	0.01
8238	PRVISE	1	0	3	0	0.01
8239	Patricia Jackson	1	0	3	0	0.01
8240	Peter Byrd	1	0	3	0	0.01
8241	Reginald Stumpf	1	0	3	0	0.01
8242	William Ashley	1	0	3	0	0.01
8243	Alicia Simmons	1	0	2	0	0.00
8244	Anne Lefebvre	1	0	2	0	0.00
8245	Christopher Conway	1	0	2	0	0.00
8246	Dominique Letellier	1	0	2	0	0.00
8247	Heidi Dudley	1	0	2	0	0.00
8248	Isaac Brown	1	0	2	0	0.00
8249	Jonathan Morris	1	0	2	0	0.00
8250	Kimberly Walker	1	0	2	0	0.00
8251	Kristen Lucero	1	0	2	0	0.00
8252	Samantha Mcneil	1	0	2	0	0.00
8253	William Torres	1	0	2	0	0.00
8254	Echelon	1	0	1	0	0.00
8255	Gudula Riehl	1	0	1	0	0.00
8256	Helmtrud Mälzer-Paffrath	1	0	1	0	0.00
8257	Michael Potts	1	0	1	0	0.00
8258	Ping Duan	1	0	1	0	0.00
8259	Yang Tao	1	0	1	0	0.00
8260	John Hayes	1	0	0	0	0.00
8261	Jędrzej Przeniosło	1	0	0	0	0.00

6.2 Question 3a) Visualize the careers of three artists. Compare and contrast their rise in popularity and influence?

Based on the results of the analysis above, the top artists in each category will be selected.

• Greatest Number of Music Released: Sailor Shift
• Most Popular Artist (Highest Number of Notable Hits): Jay Walters
• Most Influential Artist (Influence Artists and Collaborations), Most Music Influence & Highest Star Factor: Min Fu

Sailor Shift

# Extract all creators' relevant data for scaling
all_scaled_data <- creator_rankings %>%
  select(total_songs, notable_hits, collaboration_influence_creator, influence_music)

# Scale all columns to 0–100
all_scaled <- as.data.frame(lapply(all_scaled_data, scales::rescale, to = c(0, 100)))

rownames(all_scaled) <- creator_rankings$creator_name

chosen_creator_1 = "Sailor Shift"

# Filter the data for Sailor Shift
creator_1_data <- creator_rankings %>%
  filter(creator_name == chosen_creator_1) %>%
  select(total_songs, notable_hits, collaboration_influence_creator, influence_music)

# Extract scaled values for Sailor Shift
creator_1_scaled <- all_scaled[chosen_creator_1, , drop = FALSE]

# Construct radar input with min and max for the chart frame
radar_matrix_1 <- rbind(
  rep(100, ncol(creator_1_scaled)),  # Max values
  rep(0, ncol(creator_1_scaled)),    # Min values
  creator_1_scaled                   # Actual values
)

# Plot Radar Chart
par(mar = c(2, 2, 4, 2), oma = c(1, 1, 3, 1))

radarchart(radar_matrix_1,
           axistype = 1,
           pcol = "#0027EA",
           pfcol = adjustcolor("#0027EA", alpha.f = 0.3),
           plwd = 2,
           cglcol = "grey",
           cglty = 1,
           cglwd = 0.8,
           axislabcol = "black",
           caxislabels = paste0(seq(0, 100, 25), "%"),
           vlcex = 0.85,
           title = paste("Star Profile: ", chosen_creator_1),
           calcex = 0.8,
           cex.main = 1.3,
           vlabels = c("Total Music", "Notable\nHits", "Artist Influ & Colab", "Music\nInfluenced"),
           centerzero = TRUE)

Jay Walters

chosen_creator_2 = "Jay Walters"

# Filter the data for Sailor Shift
creator_2_data <- creator_rankings %>%
  filter(creator_name == chosen_creator_2) %>%
  select(total_songs, notable_hits, collaboration_influence_creator, influence_music)

# Extract scaled values for Sailor Shift
creator_2_scaled <- all_scaled[chosen_creator_2, , drop = FALSE]

# Construct radar input with min and max for the chart frame
radar_matrix_2 <- rbind(
  rep(100, ncol(creator_2_scaled)),  # Max values
  rep(0, ncol(creator_2_scaled)),    # Min values
  creator_2_scaled                   # Actual values
)

# Plot Radar Chart
par(mar = c(2, 2, 4, 2), oma = c(1, 1, 3, 1))

radarchart(radar_matrix_2,
           axistype = 1,
           pcol = "#FF5757",
           pfcol = adjustcolor("#FF5757", alpha.f = 0.3),
           plwd = 2,
           cglcol = "grey",
           cglty = 1,
           cglwd = 0.8,
           axislabcol = "black",
           caxislabels = paste0(seq(0, 100, 25), "%"),
           vlcex = 0.85,
           title = paste("Star Profile: ", chosen_creator_2),
           calcex = 0.8,
           cex.main = 1.3,
           vlabels = c("Total Music", "Notable\nHits", "Artist Influ & Colab", "Music\nInfluenced"),
           centerzero = TRUE)

Min Fu

chosen_creator_3 = "Min Fu"

# Filter the data for Sailor Shift
creator_3_data <- creator_rankings %>%
  filter(creator_name == chosen_creator_3) %>%
  select(total_songs, notable_hits, collaboration_influence_creator, influence_music)

# Extract scaled values for Sailor Shift
creator_3_scaled <- all_scaled[chosen_creator_3, , drop = FALSE]

# Construct radar input with min and max for the chart frame
radar_matrix_3 <- rbind(
  rep(100, ncol(creator_3_scaled)),  # Max values
  rep(0, ncol(creator_3_scaled)),    # Min values
  creator_3_scaled                   # Actual values
)

# Plot Radar Chart
par(mar = c(2, 2, 4, 2), oma = c(1, 1, 3, 1))

radarchart(radar_matrix_3,
           axistype = 1,
           pcol = "#A45200",
           pfcol = adjustcolor("#A45200", alpha.f = 0.3),
           plwd = 2,
           cglcol = "grey",
           cglty = 1,
           cglwd = 0.8,
           axislabcol = "black",
           caxislabels = paste0(seq(0, 100, 25), "%"),
           vlcex = 0.85,
           title = paste("Star Profile: ", chosen_creator_3),
           calcex = 0.8,
           cex.main = 1.3,
           vlabels = c("Total Music", "Notable\nHits", "Artist Influ & Colab", "Music\nInfluenced"),
           centerzero = TRUE)

6.2.1 Visualizing the career of Sailor Shift

Sailor Shift has released the most number of music (Song/Album).

# Data Preparation

chosen_creator_1 = "Sailor Shift"

# Step 1: Get the node of the chosen creator
chosen_node_1 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_1) %>%
  pull(creator_from) %>%
  unique()

# Step 2: Get the songs that the top creator produced
creator_songs_1 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_1) %>%
  pull(song_to)

# Step 3: Get the songs they have influenced / artists they collaborate with
creators_songs_collaborate_influence_1 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_1,
         infuence_music_collaborate != chosen_node_1) %>%
  pull(infuence_music_collaborate)

# Step 4: Get the songs they have influenced
creators_songs_influence_1 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_1,
         infuence_music_collaborate != chosen_node_1,
         `Edge Colour` == "Influenced By") %>%
  pull(infuence_music_collaborate)

# Step 5: Get the influenced creators of the influenced songs
creators_songs_influence_creators_1 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_1,
         influence_creator != chosen_node_1,
         !is.na(influence_creator),
         infuence_music_collaborate %in% creators_songs_influence_1) %>%
  pull(influence_creator)

all_nodes <- unique(c(chosen_node_1, 
                      creator_songs_1, 
                      creators_songs_collaborate_influence_1,
                      creators_songs_influence_creators_1))

# Create subgraph
sub_graph <- graph %>%
  filter(name %in% all_nodes)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name == chosen_creator_1, 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == chosen_creator_1, chosen_creator_1, NA)
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)

Music Releases

# Data Preparation

# Step 1: Count number of music by release date
music_by_date_1 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_1)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = music_by_date_1,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Notable Music Releases

# Data Preparation

# Step 1: Count number of notable music by release date
notable_music_by_date_1 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_1), notable == TRUE) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = notable_music_by_date_1,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Notable Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Notable Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Artist Influ & Colab

# Data Preparation

# Step 1: Count number of influenced artists by release date
influence_artists_by_date_1 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from %in% unique(chosen_node_1),
         influence_creator != unique(chosen_node_1)) %>%
  # Get unique artist-date pairs first
  distinct(influence_creator, influence_release_date) %>%
  # Find first influence date for each artist
  group_by(influence_creator) %>%
  summarize(
    first_influence_date = if(n() > 0) min(influence_release_date) else NA_real_,
    .groups = "drop"
  ) %>%
  # Count new artists by first influence date
  count(first_influence_date, name = "music_count") %>%
  arrange(first_influence_date) %>%
  rename(creator_release_date = first_influence_date) %>%
  # Calculate cumulative unique artists
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_artists_by_date_1,
  x = ~creator_release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Artists",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", creator_release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Annual Count of New Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Influenced Music

# Data Preparation

# Step 1: Count number of influenced music by release date
influence_song_by_date_1 <- mc1_nodes_clean %>%
  filter(name %in% unique(creators_songs_influence_1)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_song_by_date_1,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Music",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Influence",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Note

Sailor Shift has no influence on Music creation

6.2.2 Visualizing the career of Jay Walters

Jay Walters has the highest number of notable hits.

# Data Preparation

chosen_creator_2 = "Jay Walters"

# Step 1: Get the node of the chosen creator
chosen_node_2 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_2) %>%
  pull(creator_from) %>%
  unique()

# Step 2: Get the songs that the top creator produced
creator_songs_2 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_2) %>%
  pull(song_to)

# Step 3: Get the songs they have influenced / artists they collaborate with
creators_songs_collaborate_influence_2 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_2) %>%
  pull(infuence_music_collaborate)

# Step 4: Get the songs they have influenced
creators_songs_influence_2 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_2,
         `Edge Colour` == "Influenced By") %>%
  pull(infuence_music_collaborate)

# Step 5: Get the influenced creators of the influenced songs
creators_songs_influence_creators_2 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_2,
         influence_creator != chosen_node_1,
         !is.na(influence_creator),
         infuence_music_collaborate %in% creators_songs_influence_2) %>%
  pull(influence_creator)

all_nodes <- unique(c(chosen_node_2, 
                      creator_songs_2, 
                      creators_songs_collaborate_influence_2,
                      creators_songs_influence_creators_2))

# Create subgraph
sub_graph <- graph %>%
  filter(name %in% all_nodes)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name == chosen_creator_2, 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == chosen_creator_2, chosen_creator_2, NA)
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)

Music Releases

# Data Preparation

# Step 1: Count number of music by release date
music_by_date_2 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_2)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = music_by_date_2,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Notable Music Releases

# Data Preparation

# Step 1: Count number of notable music by release date
notable_music_by_date_2 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_2), notable == TRUE) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = notable_music_by_date_2,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Notable Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Notable Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Artist Influ & Colab

# Data Preparation

# Step 1: Count number of influenced artists by release date
influence_artists_by_date_2 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from %in% unique(chosen_node_2),
         influence_creator != unique(chosen_node_2)) %>%
  # Get unique artist-date pairs first
  distinct(influence_creator, influence_release_date) %>%
  # Find first influence date for each artist
  group_by(influence_creator) %>%
  summarize(
    first_influence_date = if(n() > 0) min(influence_release_date) else NA_real_,
    .groups = "drop"
  ) %>%
  # Count new artists by first influence date
  count(first_influence_date, name = "music_count") %>%
  arrange(first_influence_date) %>%
  rename(creator_release_date = first_influence_date) %>%
  # Calculate cumulative unique artists
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_artists_by_date_2,
  x = ~creator_release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Artists",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", creator_release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Annual Count of New Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Influenced Music

# Data Preparation

# Step 1: Count number of influenced music by release date
influence_song_by_date_2 <- mc1_nodes_clean %>%
  filter(name %in% unique(creators_songs_influence_2)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_song_by_date_2,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Music",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Influence",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

6.2.3 Visualizing the career of Min Fu

Min Fu is the most influential artist.

# Data Preparation

chosen_creator_3 = "Min Fu"

# Step 1: Get the node of the chosen creator
chosen_node_3 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_3) %>%
  pull(creator_from) %>%
  unique()

# Step 2: Get the songs that the top creator produced
creator_songs_3 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_3) %>%
  pull(song_to)

# Step 3: Get the songs they have influenced / artists they collaborate with
creators_songs_collaborate_influence_3 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_3,
         infuence_music_collaborate != chosen_node_3) %>%
  pull(infuence_music_collaborate)

# Step 4: Get the songs they have influenced
creators_songs_influence_3 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_3,
         infuence_music_collaborate != chosen_node_3,
         `Edge Colour` == "Influenced By") %>%
  pull(infuence_music_collaborate)

# Step 5: Get the influenced creators of the influenced songs
creators_songs_influence_creators_3 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_3,
         influence_creator != chosen_node_3,
         !is.na(influence_creator),
         infuence_music_collaborate %in% creators_songs_influence_3) %>%
  pull(influence_creator)

all_nodes <- unique(c(chosen_node_3, 
                      creator_songs_3, 
                      creators_songs_collaborate_influence_3,
                      creators_songs_influence_creators_3))

# Create subgraph
sub_graph <- graph %>%
  filter(name %in% all_nodes)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name == chosen_creator_3, 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == chosen_creator_3, chosen_creator_3, NA)
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)

Music Releases

# Data Preparation

# Step 1: Count number of music by release date
music_by_date_3 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_3)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = music_by_date_3,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Notable Music Releases

# Data Preparation

# Step 1: Count number of notable music by release date
notable_music_by_date_3 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_3), notable == TRUE) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = notable_music_by_date_3,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Notable Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Notable Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Artist Influ & Colab

# Data Preparation

# Step 1: Count number of influenced artists by release date
influence_artists_by_date_3 <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from %in% unique(chosen_node_3),
         influence_creator != unique(chosen_node_3)) %>%
  # Get unique artist-date pairs first
  distinct(influence_creator, influence_release_date) %>%
  # Find first influence date for each artist
  group_by(influence_creator) %>%
  summarize(
    first_influence_date = if(n() > 0) min(influence_release_date) else NA_real_,
    .groups = "drop"
  ) %>%
  # Count new artists by first influence date
  count(first_influence_date, name = "music_count") %>%
  arrange(first_influence_date) %>%
  rename(creator_release_date = first_influence_date) %>%
  # Calculate cumulative unique artists
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_artists_by_date_3,
  x = ~creator_release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Artists",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", creator_release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Annual Count of New Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Influenced Music

# Data Preparation

# Step 1: Count number of influenced music by release date
influence_song_by_date_3 <- mc1_nodes_clean %>%
  filter(name %in% unique(creators_songs_influence_3)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_song_by_date_3,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Music",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Influence",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

6.2.4 Compare and contrast the three artists’ rise in popularity and influence

The cumulative graphs of the 3 artists (Sailor Shift, Jay Walters and Min Fu) will be overlaid for comparison of their musical careers.

Music Releases

# Visualisation

plot_ly(
  data = music_by_date_1,
  x = ~release_date,
  y = ~cumulative_count,
  type = "scatter",
  mode = "lines+markers",
  name = chosen_creator_1,
  line = list(color = "#2E3192", width = 2),
  marker = list(color = "red", size = 6),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Artist: ", chosen_creator_1,
    "<br>Influence Date: ", release_date,
    "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = music_by_date_2,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_2,
    line = list(color = "green", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_2,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = music_by_date_3,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_3,
    line = list(color = "purple", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_3,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Notable Music Releases

# Visualisation

plot_ly(
  data = notable_music_by_date_1,
  x = ~release_date,
  y = ~cumulative_count,
  type = "scatter",
  mode = "lines+markers",
  name = chosen_creator_1,
  line = list(color = "#2E3192", width = 2),
  marker = list(color = "red", size = 6),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Artist: ", chosen_creator_1,
    "<br>Influence Date: ", release_date,
    "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = notable_music_by_date_2,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_2,
    line = list(color = "green", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_2,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = notable_music_by_date_3,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_3,
    line = list(color = "purple", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_3,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Notable Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Artist Influ & Colab

# Visualisation

plot_ly(
  data = influence_artists_by_date_1,
  x = ~creator_release_date,
  y = ~cumulative_count,
  type = "scatter",
  mode = "lines+markers",
  name = chosen_creator_1,
  line = list(color = "#2E3192", width = 2),
  marker = list(color = "red", size = 6),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Artist: ", chosen_creator_1,
    "<br>Influence Date: ", creator_release_date,
    "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = influence_artists_by_date_2,
    x = ~creator_release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_2,
    line = list(color = "green", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_2,
      "<br>Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = influence_artists_by_date_3,
    x = ~creator_release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_3,
    line = list(color = "purple", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_3,
      "<br>Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Annual Count of New Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Note

Sailor Shift has only Collaborations and no Artist Influence

Influenced Music

# Visualisation

plot_ly(
  data = influence_song_by_date_1,
  x = ~release_date,
  y = ~cumulative_count,
  type = "scatter",
  mode = "lines+markers",
  name = chosen_creator_1,
  line = list(color = "#2E3192", width = 2),
  marker = list(color = "red", size = 6),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Artist: ", chosen_creator_1,
    "<br>Influence Date: ", release_date,
    "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = influence_song_by_date_2,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_2,
    line = list(color = "green", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_2,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = influence_song_by_date_3,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_3,
    line = list(color = "purple", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_3,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Influence",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Note

Sailor Shift has no influence on music creation

Visualize the careers of three artists. Compare and contrast their rise in popularity and influence?

• Among the three artists, Sailor Shift is the most recent entrant to the music industry, having debuted in 2024.
  • She maintained consistent music releases through 2040.
  • With 38 total releases, she surpassed Jay Walters’ record (37 releases) in 2040, establishing herself as the most prolific artist.
  • Min Fu has 12 records.
• While Sailor Shift has released more music than Jay Walters, Jay Walters is the most popular Artist having released 35 hits that made the top record chart.
  • Sailor Shift has 25 hits while Min Fu has 12.
• Min Fu is the most influential artist having influenced/collaborated with 598 artists and influenced the creation of 192 music.
  • Jay Walters has a much lower influence having influence/collaborated with 51 artists and 50 music.
  • Sailor Shift has a similar influence of 48 artists (all collaborations) but 0 influence on the music scene.
• However, Sailor Shift has the most recent influence, with new artist connections as recent as 2040.
  • In contrast, Jay Walters’ influence period ended earlier (last connection in 2035), while Min Fu’s influence dates back to 2030.

6.3 Question 3b) Using this characterization, give three predictions of who the next Oceanus Folk stars with be over the next five years.

This will be a continuation of Question 3a where only Oceanus Folk artists and the Oceanus Folk songs that they have produced are analysed.

6.3.1 Analysing the careers of all Oceanus Folk Artists

In order to predict the next Oceanus Folk stars, a grading rubric will be used where their ranking in these 4 categories (Number of Music released, Number of Notable Music, Number of New Artists Influenced / Collaborated With and Number of Music influenced). Each category will be given equal weighting with a maximum score of 4 and the Oceanus Folk artists will be ranked using this rubric.

genre_creators = creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(song_genre == "Oceanus Folk") %>%
  pull(creator_from)

# Step 1: Filter to only Oceanus Folk Artists
oceanus_creator_and_songs_and_influences_and_creators_collaborate <- creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from %in% genre_creators, song_genre == "Oceanus Folk")

# Step 2: To highlight songs that the creator influence that is not produced by same creator
oceanus_creator_influence_lists <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  group_by(creator_name, creator_node_type, song_to, song_name, creator_release_date, song_genre, notable) %>%
  distinct () %>%
  summarize(
    unique_collaborate = list(unique(na.omit(infuence_music_collaborate[creator_from != influence_creator & `Edge Colour` == "Creator Of"]))),
    unique_influence_creators = list(unique(na.omit(influence_creator[creator_from != influence_creator & `Edge Colour` == "Influenced By"]))),
    unique_influence_music = list(unique(na.omit(infuence_music_collaborate[creator_from != influence_creator & !is.na(influence_genre)])))
  )

# Step 3: Aggregate unique influences per creator
oceanus_creator_stats <- oceanus_creator_influence_lists %>%
  group_by(creator_name) %>%
  summarize(
    total_songs = n_distinct(song_to),
    notable_hits = sum(notable == TRUE, na.rm = TRUE),
    collaboration = length(unique(unlist(unique_collaborate))),
    influence_creators = length(unique(unlist(unique_influence_creators))),
    collaboration_influence_creator = length(unique(c(unlist(unique_influence_creators),unlist( unique_collaborate)))),
    influence_music = length(unique(unlist(unique_influence_music)))
  )

# Step 4: Create Scoring Rubric
oceanus_creator_rankings <- oceanus_creator_stats %>%
  mutate(
    # Normalize each metric (0-1 scale)
    songs_score = (total_songs - min(total_songs)) / (max(total_songs) - min(total_songs)),
    notable_score = (notable_hits - min(notable_hits)) / (max(notable_hits) - min(notable_hits)),
    artists_score = (collaboration_influence_creator - min(collaboration_influence_creator)) / (max(collaboration_influence_creator) - min(collaboration_influence_creator)),
    music_score = (influence_music - min(influence_music)) / (max(influence_music) - min(influence_music)),
    
    # Calculate composite score (equal weighting)
    composite_score = songs_score + notable_score + artists_score + music_score
  ) %>%
  arrange(desc(composite_score)) %>%
  select(creator_name, total_songs, notable_hits, collaboration_influence_creator, influence_music, 
         composite_score)

# Final Ranked Table
oceanus_creator_rankings %>%
  mutate(
    Rank = row_number(),
    `Star Factor` = round(composite_score, 3)
  ) %>%
  select(Rank, creator_name, total_songs, notable_hits, collaboration_influence_creator, 
         influence_music, `Star Factor`) %>%
  rename(
    `Artist` = creator_name,
    `Total Music` = total_songs,
    `Notable Hits` = notable_hits,
    `Artist Influ & Colab` = collaboration_influence_creator,
    `Music Influenced` = influence_music
  ) %>%
  kable(caption = "Oceanus Folk Artists Ranked by Star Factor") %>%
  kable_styling("striped", full_width = F) %>%
  scroll_box(height = "300px")

Oceanus Folk Artists Ranked by Star Factor

Rank	Artist	Total Music	Notable Hits	Artist Influ & Colab	Music Influenced	Star Factor
1	Sailor Shift	36	24	41	0	2.216
2	Chao Wu	4	3	190	44	2.211
3	Xia Jia	4	3	178	42	2.102
4	Donna Caldwell	2	1	170	41	1.897
5	Xiulan Ye	2	1	170	41	1.897
6	Sandra Smith	1	0	162	39	1.739
7	Zacharie Martins	4	4	136	32	1.695
8	Constance Guibert	3	3	117	28	1.434
9	Ignazio Pastine	3	3	117	28	1.434
10	Embers of Wrath	1	1	107	26	1.196
11	Ann Holland	1	1	99	23	1.085
12	Eric Beasley	1	1	99	23	1.085
13	Jun Han	2	2	98	20	1.082
14	Chao Tan	4	4	85	15	1.041
15	Filippo Pelli	5	4	77	15	1.027
16	Lei Wan	1	1	95	20	0.996
17	Tao Ren	1	1	95	20	0.996
18	Yesenia Miller	1	1	95	20	0.996
19	Lei Tan	2	2	76	13	0.807
20	Isabella Farinelli	3	2	65	13	0.778
21	Yang Yao	3	2	63	12	0.745
22	Gang Tao	1	1	71	13	0.711
23	Li Qiu	1	1	71	13	0.711
24	Min He	1	1	71	13	0.711
25	Ping Yan	1	1	71	13	0.711
26	Wei Ma	1	1	71	13	0.711
27	Na Ren	3	3	50	10	0.673
28	Alfred Thibault	3	3	35	11	0.616
29	Synaptic Stream	3	3	35	11	0.616
30	Zoé-Agnès Delaunay	3	3	35	11	0.616
31	Wei Gao	2	2	48	11	0.615
32	Chao Qiao	2	2	52	10	0.613
33	Jun Xu	2	2	52	10	0.613
34	Vanessa Ramos	2	2	47	9	0.564
35	Lei Zeng	2	1	47	10	0.545
36	Drowned Harbor	12	5	4	0	0.544
37	Ivy Echos	11	4	13	1	0.544
38	Orla Seabloom	8	8	0	0	0.533
39	Adriana Figueroa	1	1	49	10	0.527
40	Charles Hall	1	1	49	10	0.527
41	Fang Wu	1	1	49	10	0.527
42	James Mosley	1	1	49	10	0.527
43	Thomas Dudley	1	1	49	10	0.527
44	Tao Yao	3	3	43	5	0.522
45	Juan Yu	3	3	34	7	0.520
46	Lei Jin	3	3	34	7	0.520
47	Yong Lai	3	3	34	7	0.520
48	Yong Shen	3	3	34	7	0.520
49	Guiying Dai	1	1	46	10	0.511
50	Yong Chen	1	1	46	10	0.511
51	Juan Duan	1	1	46	9	0.488
52	Tao Ye	1	1	46	9	0.488
53	Yong Guo	1	1	46	9	0.488
54	Lei Shen	2	2	36	8	0.483
55	Xia Cui	2	2	36	8	0.483
56	Xiuying Li	2	2	36	8	0.483
57	Qiang Song	3	3	35	5	0.480
58	Beatrice Albright	7	7	3	0	0.479
59	Daniel O'Connell	7	7	3	0	0.479
60	Copper Canyon Ghosts	7	7	2	0	0.474
61	Jie Cui	3	3	25	6	0.450
62	Tao Jin	4	4	24	3	0.447
63	Yong Dong	4	4	24	3	0.447
64	The Tide Singer's Knot	8	4	6	1	0.421
65	The Saltwater Weavers	9	4	4	0	0.416
66	Min Jin	2	2	27	7	0.413
67	Ming Chen	1	1	36	8	0.413
68	Lisa Martin	2	2	30	6	0.406
69	Monica Nelson	2	2	30	6	0.406
70	Guglielmo Canetta	3	3	16	6	0.403
71	Nicola Bernetti	3	3	16	6	0.403
72	Serena Lettiere	3	3	16	6	0.403
73	Vincentio Gentili	3	3	16	6	0.403
74	Sophie Ramirez	5	5	15	0	0.402
75	Maya Jensen	5	5	12	0	0.386
76	Georgina Piñol-Guerra	1	0	43	7	0.385
77	Lalo Salgado Cazorla	1	0	43	7	0.385
78	Luigina Stoppani-Sandi	1	0	43	7	0.385
79	Tao Lei	2	2	30	5	0.383
80	The Brine Choir	7	4	6	0	0.370
81	Aneta Pruschke-Sölzer	2	2	23	6	0.369
82	Cesare Cicilia	2	2	23	6	0.369
83	Courtney Phillips	1	1	32	7	0.369
84	Jennifer Diaz	1	1	32	7	0.369
85	Kelli Turner	1	1	32	7	0.369
86	Latasha Chavez	1	1	32	7	0.369
87	Min Qian	1	1	32	7	0.369
88	Na Wen	3	3	21	3	0.361
89	Thibault Hardy	2	2	21	6	0.359
90	Coralia Bellweather	5	5	6	0	0.354
91	Bryan Smith	1	1	33	6	0.352
92	Emily Jackson	1	1	33	6	0.352
93	Gregory Williams	1	1	33	6	0.352
94	Ronald Kennedy	1	1	33	6	0.352
95	Adèle Bonneau	2	2	19	6	0.348
96	James Clark	1	1	30	6	0.336
97	Kristin Anderson	1	1	30	6	0.336
98	Ming Su	1	1	30	6	0.336
99	Guiying Liao	2	2	18	5	0.320
100	Ping Sun	2	2	18	5	0.320
101	Yan Zou	2	2	18	5	0.320
102	Jie Dai	1	1	27	6	0.320
103	Juan Gu	1	1	27	6	0.320
104	Ping Pan	1	1	27	6	0.320
105	Qiang Dai	1	1	27	6	0.320
106	Tao Chang	1	1	27	6	0.320
107	Xiulan He	1	1	27	6	0.320
108	Na Yang	2	2	25	3	0.312
109	Lauretta Tresoldi	2	2	20	4	0.308
110	Xiulan Xu	2	2	17	4	0.292
111	Wei Guo	2	2	21	3	0.291
112	Levi Holloway	4	4	7	0	0.289
113	Jie Li	2	2	20	3	0.285
114	Tao Dai	2	2	20	3	0.285
115	Jonah Calloway	4	4	6	0	0.284
116	Marin Thorne	4	4	6	0	0.284
117	Alphons Donati-Pederiva	2	2	19	3	0.280
118	Augusto Villaverde Armengol	2	2	19	3	0.280
119	Feliciano Pizarro Moll	2	2	19	3	0.280
120	Juan Dong	2	2	19	3	0.280
121	Jing Long	1	1	28	4	0.280
122	Tao Qin	1	1	28	4	0.280
123	Xiuying Xiang	1	1	28	4	0.280
124	Yan Feng	1	1	28	4	0.280
125	Barnaby Quill	4	4	4	0	0.273
126	Jonas Fenwick	4	4	4	0	0.273
127	Marina Grey	4	4	4	0	0.273
128	Orin Caldwell	4	4	4	0	0.273
129	Selena Marrow	4	4	4	0	0.273
130	Silas Finch	4	4	4	0	0.273
131	Thorne Rivenhall	4	4	4	0	0.273
132	Brandy Reyes	1	1	26	4	0.269
133	Juan Qiao	1	1	26	4	0.269
134	Jun Jin	1	1	26	4	0.269
135	Laura Gibbs	1	1	26	4	0.269
136	Mary Medina	1	1	26	4	0.269
137	Na Ma	1	1	26	4	0.269
138	Raymond Mccoy	1	1	26	4	0.269
139	Rhonda Brown	1	1	26	4	0.269
140	Xiulan Ren	1	1	26	4	0.269
141	Na Peng	2	1	29	2	0.268
142	Gang Zhu	2	2	21	2	0.268
143	Ming Yu	2	2	21	2	0.268
144	Jing Zhong	2	2	15	3	0.259
145	Albert Miller	2	2	19	2	0.257
146	Adrien Weiss	1	1	23	4	0.254
147	Charles du Perez	1	1	23	4	0.254
148	Pauline Guyot de la Maillet	1	1	23	4	0.254
149	Qiang Qian	2	2	18	2	0.252
150	Walter White	2	2	18	2	0.252
151	Xia Xia	2	2	18	2	0.252
152	Édouard-Rémy Clerc	1	1	18	5	0.250
153	Qiang Xie	2	2	17	2	0.247
154	Xiuying Huang	2	2	17	2	0.247
155	Chao Xiao	2	2	16	2	0.242
156	Laetitia Petitjean	2	2	11	3	0.238
157	Madeleine Pineau	2	2	11	3	0.238
158	Susan Da Silva	2	2	11	3	0.238
159	Émilie Maillet	2	2	11	3	0.238
160	Antonietta Zamengo-Zanazzo	1	1	20	4	0.238
161	Patrizio Antonello-Salvemini	1	1	20	4	0.238
162	Xiulan Mo	3	3	9	0	0.230
163	Tidal Reverie	5	2	6	0	0.229
164	Jade Thompson	3	3	8	0	0.224
165	Lila "Lilly" Hartman	3	3	8	0	0.224
166	Ping Peng	2	2	12	2	0.221
167	Ethan Sanchez	1	1	21	3	0.220
168	Fang Gu	1	1	21	3	0.220
169	Ping Cui	1	1	21	3	0.220
170	Xiuying Yin	1	1	21	3	0.220
171	Yan Fang	1	1	21	3	0.220
172	Yan Liang	1	1	21	3	0.220
173	Selkie's Hollow	5	2	4	0	0.219
174	Wei Qian	2	2	11	2	0.215
175	Chao Tang	1	1	20	3	0.215
176	Gang Yin	1	1	20	3	0.215
177	Gang Zhang	1	1	20	3	0.215
178	Guiying Ding	1	1	20	3	0.215
179	Li Zou	1	1	20	3	0.215
180	Na Zou	1	1	20	3	0.215
181	Wei Chang	1	1	20	3	0.215
182	Xiuying Du	1	1	20	3	0.215
183	Arlo Sterling	3	3	6	0	0.214
184	Cassian Rae	3	3	6	0	0.214
185	Elara May	3	3	6	0	0.214
186	Lyra Blaze	3	3	6	0	0.214
187	Orion Cruz	3	3	6	0	0.214
188	Yan Zhou	2	2	15	1	0.214
189	Coriolano Luria-Scialpi	1	1	15	4	0.212
190	Arnaude Poirier	1	1	19	3	0.210
191	Christophe-Émile Grégoire	1	1	19	3	0.210
192	Georges Chrétien	1	1	19	3	0.210
193	Interval Shift	1	1	19	3	0.210
194	Luce Loiseau	1	1	19	3	0.210
195	Martino Michelangeli	1	1	19	3	0.210
196	Caleb Foster	3	3	5	0	0.208
197	Eleanor Wren	3	3	5	0	0.208
198	Kai Reynolds	3	3	5	0	0.208
199	Jie Xu	2	2	13	1	0.203
200	Na Dai	2	2	13	1	0.203
201	Xia Wu	2	2	13	1	0.203
202	Christine Ward	1	1	17	3	0.199
203	Donald Medina	1	1	17	3	0.199
204	Erik Mathews	1	1	17	3	0.199
205	Gang Shao	1	1	17	3	0.199
206	Jie Fan	1	1	17	3	0.199
207	Joshua Orozco	1	1	17	3	0.199
208	Jun Pan	1	1	17	3	0.199
209	Lisa Hofmann	1	1	17	3	0.199
210	Min Liang	1	1	17	3	0.199
211	Na Li	1	1	17	3	0.199
212	Na Yi	1	1	17	3	0.199
213	Sonora Flux	1	1	17	3	0.199
214	Steven Goodman	1	1	17	3	0.199
215	Wei Liao	1	1	17	3	0.199
216	Xia Shao	1	1	17	3	0.199
217	Xia Wang	1	1	17	3	0.199
218	Xiulan Lin	1	1	17	3	0.199
219	Yang Wu	1	1	17	3	0.199
220	The Wave Riders	4	2	5	0	0.195
221	Michael Reyes	2	2	7	2	0.194
222	Michael Rivera	2	2	7	2	0.194
223	Eric Hopkins	1	1	16	3	0.194
224	Guiying Tao	1	1	16	3	0.194
225	Min Bai	1	1	16	3	0.194
226	Ming Qiao	1	1	16	3	0.194
227	Ryan Mcguire	1	1	16	3	0.194
228	Stephen Allen	1	1	16	3	0.194
229	Yang Dai	1	1	16	3	0.194
230	Jun Yuan	2	2	11	1	0.193
231	Jing Xu	1	1	15	3	0.189
232	Juan Wen	1	1	15	3	0.189
233	Min Xue	1	1	15	3	0.189
234	Min Yu	1	1	15	3	0.189
235	Loro	2	2	10	1	0.187
236	Xiulan Yi	2	2	10	1	0.187
237	Siren's Call	4	2	3	0	0.185
238	Xiulan Dong	2	2	9	1	0.182
239	Yong Tang	2	2	9	1	0.182
240	Mariner's Refrain	4	1	6	1	0.182
241	Rüdiger Graf	2	1	12	2	0.179
242	Na Zhong	2	2	8	1	0.177
243	Evelyn Gordon	1	1	17	2	0.177
244	Gang Wang	1	1	17	2	0.177
245	Jie Yan	1	1	17	2	0.177
246	Min Gao	1	1	17	2	0.177
247	Richard Frazier	1	1	17	2	0.177
248	Sonia Weeks	1	1	17	2	0.177
249	Wei Cheng	1	1	17	2	0.177
250	Yan Hou	1	1	17	2	0.177
251	Lei Tao	1	0	24	2	0.172
252	Ming Song	1	0	24	2	0.172
253	Nadia van Dooren-de Jode Vastraedsd	1	0	24	2	0.172
254	Tao Zhao	1	0	24	2	0.172
255	Eric Bartlett	1	1	16	2	0.171
256	Min Yan	1	1	16	2	0.171
257	Xia Zeng	1	1	16	2	0.171
258	Yong Tao	2	2	11	0	0.170
259	Joker's Smile	1	1	7	4	0.169
260	Genevieve Bell	4	2	0	0	0.169
261	Chao Zeng	1	1	11	3	0.168
262	Gang Jiang	1	1	11	3	0.168
263	Xia Xiang	1	1	11	3	0.168
264	Guiying Ma	1	1	15	2	0.166
265	Jie Wu	1	1	15	2	0.166
266	Jing Li	1	1	15	2	0.166
267	Na Qian	1	1	15	2	0.166
268	Yong Wen	1	1	15	2	0.166
269	Jun Yi	1	1	14	2	0.161
270	Xia Yu	1	1	14	2	0.161
271	Min Huang	2	2	9	0	0.159
272	Yang Ma	2	2	9	0	0.159
273	David Nash	1	1	13	2	0.156
274	Fang Duan	1	1	13	2	0.156
275	Gang Yang	1	1	13	2	0.156
276	Jessica Beck	1	1	13	2	0.156
277	Kaori Kobayashi	1	1	13	2	0.156
278	Wei Lai	1	1	13	2	0.156
279	Xia Liu	1	1	13	2	0.156
280	Yong Dai	1	1	13	2	0.156
281	Yui Kondo	1	1	13	2	0.156
282	Juan Wu	2	2	8	0	0.154
283	Rusty Riggins	2	2	8	0	0.154
284	Ella Parker	3	1	6	1	0.153
285	Guiying Cao	1	1	12	2	0.150
286	Jing Kang	1	1	12	2	0.150
287	Luciano Chindamo	1	1	12	2	0.150
288	Silver Seraph	1	1	12	2	0.150
289	Chao Qiu	2	2	7	0	0.149
290	Yoko Fujita	2	2	7	0	0.149
291	Chao Zhou	1	1	11	2	0.145
292	Fenna Zwart	1	1	11	2	0.145
293	Kevin Ruiz	1	1	11	2	0.145
294	Kimberly Stanton	1	1	11	2	0.145
295	Lei Wei	1	1	11	2	0.145
296	Michael Anderson	1	1	11	2	0.145
297	Xiulan Gu	1	1	11	2	0.145
298	Ethan Clarke	2	2	6	0	0.143
299	Kara Lee	2	2	6	0	0.143
300	Michael Harris	2	2	6	0	0.143
301	Sophie Bennett	2	2	6	0	0.143
302	Tao Cui	2	2	6	0	0.143
303	Jose Garrett	1	1	10	2	0.140
304	Juan Pan	1	1	10	2	0.140
305	Juan Zeng	1	1	10	2	0.140
306	Lupe Miró	1	1	10	2	0.140
307	Ming Wan	1	1	10	2	0.140
308	Paulo Oestrovsky	1	1	10	2	0.140
309	Qiang He	1	1	10	2	0.140
310	Qiang Lai	1	1	10	2	0.140
311	Qiang Zhu	1	1	10	2	0.140
312	Aiden Harper	2	2	5	0	0.138
313	Finn Morgan	2	2	5	0	0.138
314	Skylar Brooks	2	2	5	0	0.138
315	Christine Turner	1	1	9	2	0.134
316	Gang Zeng	1	1	9	2	0.134
317	Li Dai	1	1	9	2	0.134
318	Min Ding	1	1	9	2	0.134
319	Nicholas Porter	1	1	9	2	0.134
320	Victoria Higgins	1	1	9	2	0.134
321	Xiuying Jia	1	1	9	2	0.134
322	Jing Guo	1	1	13	1	0.133
323	Li Wan	1	1	13	1	0.133
324	Mandy Hood	1	1	13	1	0.133
325	Min Kong	1	1	13	1	0.133
326	Operatic Pulse	1	1	13	1	0.133
327	Robert Villanueva	1	1	13	1	0.133
328	Yang Zhao	1	1	13	1	0.133
329	Carlos Duffy	2	2	3	0	0.128
330	Ewan MacCrae	2	2	3	0	0.128
331	Freya Lindholm	2	2	3	0	0.128
332	Justin Berry	2	2	3	0	0.128
333	Lila Rivers	2	2	3	0	0.128
334	Mia Waters	2	2	3	0	0.128
335	Mikayla Cook	2	2	3	0	0.128
336	Min Qin	2	2	3	0	0.128
337	Ming Du	1	1	12	1	0.128
338	Morgan Hernandez	1	1	12	1	0.128
339	Patricia Pope	1	1	12	1	0.128
340	Ping Shao	1	1	12	1	0.128
341	Yang Liang	1	1	12	1	0.128
342	Gang Fu	1	1	6	2	0.119
343	Li Long	1	1	6	2	0.119
344	Ping Zeng	1	1	6	2	0.119
345	Chao Fang	1	1	10	1	0.117
346	Gang Ding	1	1	10	1	0.117
347	Guiying Jin	1	1	10	1	0.117
348	Min Lei	1	1	10	1	0.117
349	Nicholas Moss	1	1	10	1	0.117
350	Qiang Jiang	1	1	10	1	0.117
351	Rachel Rios	1	1	10	1	0.117
352	Tammie Johnson	1	1	10	1	0.117
353	Xia Shen	1	1	10	1	0.117
354	Xiulan Zhou	1	1	10	1	0.117
355	Yan Chang	1	1	10	1	0.117
356	Yang Zhang	1	1	10	1	0.117
357	Beppe Agazzi-Muratori	1	1	9	1	0.112
358	Chao Jin	1	1	9	1	0.112
359	Chiyo Suzuki	1	1	9	1	0.112
360	Elena Nibali	1	1	9	1	0.112
361	Gang Bai	1	1	9	1	0.112
362	Jeffery Bailey	1	1	9	1	0.112
363	Jing Wei	1	1	9	1	0.112
364	Kana Mori	1	1	9	1	0.112
365	Kaori Watanabe	1	1	9	1	0.112
366	Karen Robinson	1	1	9	1	0.112
367	Li Yang	1	1	9	1	0.112
368	Min Zheng	1	1	9	1	0.112
369	Ming Wang	1	1	9	1	0.112
370	Ming Xiang	1	1	9	1	0.112
371	Ming Yan	1	1	9	1	0.112
372	Na Deng	1	1	9	1	0.112
373	Renata Manolesso	1	1	9	1	0.112
374	Taichi Sasaki	1	1	9	1	0.112
375	Tao Hu	1	1	9	1	0.112
376	Tao Zhang	1	1	9	1	0.112
377	Tomoya Nakamura	1	1	9	1	0.112
378	Tomoya Takahashi	1	1	9	1	0.112
379	Wei Chen	1	1	9	1	0.112
380	Xiulan Wang	1	1	9	1	0.112
381	Xiuying Kang	1	1	9	1	0.112
382	Yan Luo	1	1	9	1	0.112
383	Yuki Hashimoto	1	1	9	1	0.112
384	Zaira Balotelli-Peano	1	1	9	1	0.112
385	Eduardo Gonzalez	1	1	8	1	0.106
386	Guiying Qin	1	1	8	1	0.106
387	Justin Morse	1	1	8	1	0.106
388	Li He	1	1	8	1	0.106
389	Na Jin	1	1	8	1	0.106
390	Theresa Rivera	1	1	8	1	0.106
391	William Lynch	1	1	8	1	0.106
392	Amanda Ramos	1	1	7	1	0.101
393	Deborah Davis	1	1	7	1	0.101
394	Jie Chang	1	1	7	1	0.101
395	Jie Wan	1	1	7	1	0.101
396	Jie Wang	1	1	7	1	0.101
397	Kent Chen	1	1	7	1	0.101
398	Lei Jia	1	1	7	1	0.101
399	Li Gu	1	1	7	1	0.101
400	Min Liao	1	1	7	1	0.101
401	Min Zou	1	1	7	1	0.101
402	Regina Davis	1	1	7	1	0.101
403	Russell Bates	1	1	7	1	0.101
404	Tao Qian	1	1	7	1	0.101
405	Tao Su	1	1	7	1	0.101
406	Teresa Rivera	1	1	7	1	0.101
407	Whitney Humphrey	1	1	7	1	0.101
408	Xia Du	1	1	7	1	0.101
409	Xiuying Deng	1	1	7	1	0.101
410	Xiuying Fan	1	1	7	1	0.101
411	Xiuying Zhang	1	1	7	1	0.101
412	Yang Wan	1	1	7	1	0.101
413	Alexej Klotz	1	1	6	1	0.096
414	Autumn Walker	1	1	6	1	0.096
415	Christopher Smith	1	1	6	1	0.096
416	Elsbeth Löwer	1	1	6	1	0.096
417	Gang Meng	1	1	6	1	0.096
418	Guiying Pan	1	1	6	1	0.096
419	Jing Ding	1	1	6	1	0.096
420	Jing Zhang	1	1	6	1	0.096
421	Lagan Bhalla	1	1	6	1	0.096
422	Leif Bien	1	1	6	1	0.096
423	Li Xie	1	1	6	1	0.096
424	Li Zeng	1	1	6	1	0.096
425	Margaretha Sager	1	1	6	1	0.096
426	Nakul Samra	1	1	6	1	0.096
427	Ping Zheng	1	1	6	1	0.096
428	Timothy Hansen	1	1	6	1	0.096
429	Xia Zhu	1	1	6	1	0.096
430	Xiuying Duan	1	1	6	1	0.096
431	Jing Luo	1	1	5	1	0.091
432	Lei Qiu	1	1	5	1	0.091
433	Matthew Murphy	1	1	5	1	0.091
434	Qiang Cui	1	1	5	1	0.091
435	Xiulan Zhong	1	1	5	1	0.091
436	Ann Anderson	1	1	4	1	0.085
437	Min Mo	1	1	4	1	0.085
438	Ming Zhong	1	1	4	1	0.085
439	Ping Liao	1	1	4	1	0.085
440	Xiuying Mo	1	1	4	1	0.085
441	Finn McGraw	1	1	8	0	0.084
442	William Tidewell	1	1	8	0	0.084
443	Fang Yan	1	1	7	0	0.079
444	Jie Dong	1	1	7	0	0.079
445	Jie Long	1	1	7	0	0.079
446	Jing Chang	1	1	7	0	0.079
447	Juan Cao	1	1	7	0	0.079
448	Juan Xiang	1	1	7	0	0.079
449	Jun Lai	1	1	7	0	0.079
450	Li Dong	1	1	7	0	0.079
451	Min Guo	1	1	7	0	0.079
452	Qiang Gao	1	1	7	0	0.079
453	Qiang Sun	1	1	7	0	0.079
454	Tao Xu	1	1	7	0	0.079
455	Wei Yao	1	1	7	0	0.079
456	Yong Qiao	1	1	7	0	0.079
457	Leo Hamilton	2	0	5	1	0.078
458	Noah Bennett	2	0	5	1	0.078
459	Min Song	1	1	2	1	0.075
460	Yong Xia	1	1	2	1	0.075
461	Christopher Lowery	1	1	6	0	0.073
462	Ela Batra	1	1	6	0	0.073
463	Guiying Dong	1	1	6	0	0.073
464	Guiying Tang	1	1	6	0	0.073
465	Guiying Xia	1	1	6	0	0.073
466	Guiying Zhong	1	1	6	0	0.073
467	Jun Qiao	1	1	6	0	0.073
468	Jun Shao	1	1	6	0	0.073
469	Kimaya Srinivasan	1	1	6	0	0.073
470	Lei Liao	1	1	6	0	0.073
471	Lei Qian	1	1	6	0	0.073
472	Li Ding	1	1	6	0	0.073
473	Melissa Wheeler	1	1	6	0	0.073
474	Ming Yao	1	1	6	0	0.073
475	Na Cai	1	1	6	0	0.073
476	Na Guo	1	1	6	0	0.073
477	Na Jiang	1	1	6	0	0.073
478	Onkar Kata	1	1	6	0	0.073
479	Qiang Du	1	1	6	0	0.073
480	Qiang Yang	1	1	6	0	0.073
481	Ryan Devan	1	1	6	0	0.073
482	Tami Cox	1	1	6	0	0.073
483	Tao Long	1	1	6	0	0.073
484	Tara Kota	1	1	6	0	0.073
485	Thomas Ball	1	1	6	0	0.073
486	Tiya Sani	1	1	6	0	0.073
487	Wei Bai	1	1	6	0	0.073
488	Xia Kang	1	1	6	0	0.073
489	Yuvraj Dar	1	1	6	0	0.073
490	Amber Smith	1	1	5	0	0.068
491	Cardboard Twenty-Five	1	1	5	0	0.068
492	Chao Kang	1	1	5	0	0.068
493	Chao Wan	1	1	5	0	0.068
494	Chao Zheng	1	1	5	0	0.068
495	Cosmic Drift	1	1	5	0	0.068
496	Crimson Theorem	1	1	5	0	0.068
497	Daria Ceravolo	1	1	5	0	0.068
498	Fang Han	1	1	5	0	0.068
499	Fang Qiao	1	1	5	0	0.068
500	Fang Zhong	1	1	5	0	0.068
501	Fiesta Nocturna	1	1	5	0	0.068
502	Gang Fan	1	1	5	0	0.068
503	Guiying Qiu	1	1	5	0	0.068
504	Guiying Ye	1	1	5	0	0.068
505	Jie Bai	1	1	5	0	0.068
506	Jie Su	1	1	5	0	0.068
507	Jing Cui	1	1	5	0	0.068
508	Joseph Cole	1	1	5	0	0.068
509	Jun Fu	1	1	5	0	0.068
510	Jun Ren	1	1	5	0	0.068
511	Jun Wen	1	1	5	0	0.068
512	Li Li	1	1	5	0	0.068
513	Li Qiao	1	1	5	0	0.068
514	Lisa Pennington	1	1	5	0	0.068
515	Liwia Piejko	1	1	5	0	0.068
516	Min Tian	1	1	5	0	0.068
517	Min Xiong	1	1	5	0	0.068
518	Ming Qiu	1	1	5	0	0.068
519	Na Han	1	1	5	0	0.068
520	Nicole Thomas	1	1	5	0	0.068
521	Ping Meng	1	1	5	0	0.068
522	Ping Tao	1	1	5	0	0.068
523	Rachel Ford	1	1	5	0	0.068
524	Ryan Adams	1	1	5	0	0.068
525	Stella Fusani	1	1	5	0	0.068
526	Tao Xiong	1	1	5	0	0.068
527	Tao Yin	1	1	5	0	0.068
528	Xiuying Shao	1	1	5	0	0.068
529	Yan Huang	1	1	5	0	0.068
530	Yang Lei	1	1	5	0	0.068
531	Yong Wang	1	1	5	0	0.068
532	Jennifer Alexander	1	0	8	1	0.065
533	Lauren Steele	1	0	8	1	0.065
534	Lori Young	1	0	8	1	0.065
535	Sean Riggs	1	0	8	1	0.065
536	Abigail Hawkins	1	1	4	0	0.063
537	Amanda Coleman	1	1	4	0	0.063
538	Andrew Lee	1	1	4	0	0.063
539	Carly Luna	1	1	4	0	0.063
540	Chao Cheng	1	1	4	0	0.063
541	Chao Lu	1	1	4	0	0.063
542	Chao Sun	1	1	4	0	0.063
543	Charles Vazquez	1	1	4	0	0.063
544	Chelsea Harris	1	1	4	0	0.063
545	David Morgan	1	1	4	0	0.063
546	Devin Chan	1	1	4	0	0.063
547	Donna Ryan	1	1	4	0	0.063
548	Fang Luo	1	1	4	0	0.063
549	Fang Zhou	1	1	4	0	0.063
550	Frédéric de la Coulon	1	1	4	0	0.063
551	Gang Lai	1	1	4	0	0.063
552	Gang Tian	1	1	4	0	0.063
553	Gang Yuan	1	1	4	0	0.063
554	Guiying Fan	1	1	4	0	0.063
555	Guiying Yang	1	1	4	0	0.063
556	Jenna York	1	1	4	0	0.063
557	Jennifer Harris	1	1	4	0	0.063
558	Jie Zhong	1	1	4	0	0.063
559	Jing Xiang	1	1	4	0	0.063
560	Juan Chen	1	1	4	0	0.063
561	Juan Han	1	1	4	0	0.063
562	Juan Wang	1	1	4	0	0.063
563	Jörg Niemeier	1	1	4	0	0.063
564	Kimberly Moore	1	1	4	0	0.063
565	Kinship Harmony	1	1	4	0	0.063
566	Lei Peng	1	1	4	0	0.063
567	Lei Tang	1	1	4	0	0.063
568	Lei Xue	1	1	4	0	0.063
569	Li Bai	1	1	4	0	0.063
570	Li Deng	1	1	4	0	0.063
571	Lia Grant	1	1	4	0	0.063
572	Mary King	1	1	4	0	0.063
573	Mary Parsons	1	1	4	0	0.063
574	Maurice Harrison	1	1	4	0	0.063
575	Michael Gregory	1	1	4	0	0.063
576	Michael Jefferson	1	1	4	0	0.063
577	Michelle Weiss	1	1	4	0	0.063
578	Min Xia	1	1	4	0	0.063
579	Ming Liu	1	1	4	0	0.063
580	Mitchell Bryan	1	1	4	0	0.063
581	Paul-Louis Benard	1	1	4	0	0.063
582	Ping Chang	1	1	4	0	0.063
583	Ping Feng	1	1	4	0	0.063
584	Ping Wu	1	1	4	0	0.063
585	Ping Xu	1	1	4	0	0.063
586	Ricardo Ward	1	1	4	0	0.063
587	Scott Burke	1	1	4	0	0.063
588	Tao Du	1	1	4	0	0.063
589	Tao Zou	1	1	4	0	0.063
590	The Nulls	1	1	4	0	0.063
591	Valérie Mathieu	1	1	4	0	0.063
592	Victoria Vazquez	1	1	4	0	0.063
593	Xia Ye	1	1	4	0	0.063
594	Xia Zhao	1	1	4	0	0.063
595	Xiulan Hu	1	1	4	0	0.063
596	Xiulan Lu	1	1	4	0	0.063
597	Yan Yan	1	1	4	0	0.063
598	Yang Mo	1	1	4	0	0.063
599	Yang Shao	1	1	4	0	0.063
600	Yong Wu	1	1	4	0	0.063
601	Zachary Cole	1	1	4	0	0.063
602	Éric Lamy	1	1	4	0	0.063
603	Amber Ramsey	1	1	3	0	0.057
604	Amedeo Mimun	1	1	3	0	0.057
605	Angela Perez	1	1	3	0	0.057
606	Astrid Nørgaard	1	1	3	0	0.057
607	Chad Marks	1	1	3	0	0.057
608	Christine Brown	1	1	3	0	0.057
609	Christine Jones	1	1	3	0	0.057
610	Christopher Reilly	1	1	3	0	0.057
611	Daniel Oneill	1	1	3	0	0.057
612	Deborah Norton	1	1	3	0	0.057
613	Edward Little	1	1	3	0	0.057
614	Fang Xiang	1	1	3	0	0.057
615	Gang Shen	1	1	3	0	0.057
616	Ida Vigliotti	1	1	3	0	0.057
617	Jacob Arnold	1	1	3	0	0.057
618	Jeffrey Watson	1	1	3	0	0.057
619	Jie Cheng	1	1	3	0	0.057
620	Jimmy Arnold	1	1	3	0	0.057
621	Jing Xiao	1	1	3	0	0.057
622	Juan Cheng	1	1	3	0	0.057
623	Juan Deng	1	1	3	0	0.057
624	Juan Kang	1	1	3	0	0.057
625	Jun Hao	1	1	3	0	0.057
626	Jun Wang	1	1	3	0	0.057
627	Jun Ye	1	1	3	0	0.057
628	Jun Zou	1	1	3	0	0.057
629	Latasha Watts	1	1	3	0	0.057
630	Lei Su	1	1	3	0	0.057
631	Lei Zheng	1	1	3	0	0.057
632	Li Zhou	1	1	3	0	0.057
633	Margherita Giammusso	1	1	3	0	0.057
634	Monica Murphy	1	1	3	0	0.057
635	Pina Garrone	1	1	3	0	0.057
636	Ping Bai	1	1	3	0	0.057
637	Ping Song	1	1	3	0	0.057
638	Qiang Han	1	1	3	0	0.057
639	Qiang Mo	1	1	3	0	0.057
640	Susan Randall	1	1	3	0	0.057
641	Tao Shen	1	1	3	0	0.057
642	Xiulan Lai	1	1	3	0	0.057
643	Yan Wang	1	1	3	0	0.057
644	Yan Zhu	1	1	3	0	0.057
645	Yang Qian	1	1	3	0	0.057
646	Yong Feng	1	1	3	0	0.057
647	Andrew Carrillo	1	1	2	0	0.052
648	Chao Mo	1	1	2	0	0.052
649	Douglas Roberts	1	1	2	0	0.052
650	Fiona Mercer	1	1	2	0	0.052
651	Gang Lin	1	1	2	0	0.052
652	Gang Pan	1	1	2	0	0.052
653	Isaiah Morales	1	1	2	0	0.052
654	Jason Vaughn	1	1	2	0	0.052
655	Jill Smith	1	1	2	0	0.052
656	John Maldonado	1	1	2	0	0.052
657	Juan Gong	1	1	2	0	0.052
658	Jun Yu	1	1	2	0	0.052
659	Katelyn Johnson	1	1	2	0	0.052
660	Kyle Lawrence	1	1	2	0	0.052
661	Li Wen	1	1	2	0	0.052
662	Li Xiong	1	1	2	0	0.052
663	Liam O'Sullivan	1	1	2	0	0.052
664	Mary Morales	1	1	2	0	0.052
665	Michael Snyder	1	1	2	0	0.052
666	Michaela Brooks	1	1	2	0	0.052
667	Michelangelo Berlusconi	1	1	2	0	0.052
668	Ming Zhou	1	1	2	0	0.052
669	Miss Erin Dean	1	1	2	0	0.052
670	Na Lai	1	1	2	0	0.052
671	Nancy Arnold	1	1	2	0	0.052
672	Raymond Martin	1	1	2	0	0.052
673	Richard Mckenzie	1	1	2	0	0.052
674	Stephanie Benjamin	1	1	2	0	0.052
675	Stephen Meyer	1	1	2	0	0.052
676	Steven Williams	1	1	2	0	0.052
677	Summer Bender	1	1	2	0	0.052
678	Tao Kong	1	1	2	0	0.052
679	Wei Zou	1	1	2	0	0.052
680	Xia Yan	1	1	2	0	0.052
681	Xiulan Liang	1	1	2	0	0.052
682	Xiulan Zhang	1	1	2	0	0.052
683	Xiuying Xie	1	1	2	0	0.052
684	Yan Tao	1	1	2	0	0.052
685	Yang Zhong	1	1	2	0	0.052
686	Yong Ding	1	1	2	0	0.052
687	Yong Tan	1	1	2	0	0.052
688	Bronze Tiger	1	0	5	1	0.049
689	Dustin Maxwell	1	1	1	0	0.047
690	Erin Martinez	1	1	1	0	0.047
691	Ishaan Bal	1	1	1	0	0.047
692	Isla Quinn	1	1	1	0	0.047
693	Jessica Carney	1	1	1	0	0.047
694	Jun Bai	1	1	1	0	0.047
695	Lei Kong	1	1	1	0	0.047
696	Michael Smith	1	1	1	0	0.047
697	Ping Zhao	1	1	1	0	0.047
698	Riya Dhingra	1	1	1	0	0.047
699	Shlok Cherian	1	1	1	0	0.047
700	Tanya Garrett	1	1	1	0	0.047
701	Tara Deshmukh	1	1	1	0	0.047
702	William Knapp	1	1	1	0	0.047
703	Xiulan Hao	1	1	1	0	0.047
704	Xiuying Liang	1	1	1	0	0.047
705	Yang Gong	1	1	1	0	0.047
706	Jing Feng	1	1	0	0	0.042
707	Guiying Gong	1	0	6	0	0.032
708	Jing Jin	1	0	6	0	0.032
709	Ming Deng	1	0	6	0	0.032
710	Ming Jia	1	0	6	0	0.032
711	Tao Xue	1	0	6	0	0.032
712	Whispered Resonance	1	0	6	0	0.032
713	Xiuying Fang	1	0	6	0	0.032
714	Gang Xiong	1	0	3	0	0.016
715	Li Huang	1	0	3	0	0.016
716	Qiang Luo	1	0	3	0	0.016
717	Yong Ye	1	0	3	0	0.016
718	Jonathan Morris	1	0	2	0	0.011
719	Ming Xiao	1	0	2	0	0.011
720	Xiuying Wei	1	0	2	0	0.011

6.3.2 Visualizing the career of Sailor Shift

# Data Preparation

chosen_creator_1 = "Sailor Shift"

# Step 1: Get the node of the chosen creator
chosen_node_1 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_1) %>%
  pull(creator_from) %>%
  unique()

# Step 2: Get the songs that the top creator produced
creator_songs_1 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_1) %>%
  pull(song_to)

# Step 3: Get the songs they have influenced / artists they collaborate with
creators_songs_collaborate_influence_1 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_1,
         infuence_music_collaborate != chosen_node_1) %>%
  pull(infuence_music_collaborate)

# Step 4: Get the songs they have influenced
creators_songs_influence_1 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_1,
         infuence_music_collaborate != chosen_node_1,
         `Edge Colour` == "Influenced By") %>%
  pull(infuence_music_collaborate)

# Step 5: Get the influenced creators of the influenced songs
creators_songs_influence_creators_1 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_1,
         influence_creator != chosen_node_1,
         !is.na(influence_creator),
         infuence_music_collaborate %in% creators_songs_influence_1) %>%
  pull(influence_creator)

all_nodes <- unique(c(chosen_node_1, 
                      creator_songs_1, 
                      creators_songs_collaborate_influence_1,
                      creators_songs_influence_creators_1))

# Create subgraph
sub_graph <- graph %>%
  filter(name %in% all_nodes)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name == chosen_creator_1, 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == chosen_creator_1, chosen_creator_1, NA)
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)

Music Releases

# Data Preparation

# Step 1: Count number of music by release date
music_by_date_1 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_1)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = music_by_date_1,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Notable Music Releases

# Data Preparation

# Step 1: Count number of notable music by release date
notable_music_by_date_1 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_1), notable == TRUE) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = notable_music_by_date_1,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Notable Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Notable Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Artist Influ & Colab

# Data Preparation

# Step 1: Count number of influenced artists by release date
influence_artists_by_date_1 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from %in% unique(chosen_node_1),
         influence_creator != unique(chosen_node_1)) %>%
  # Get unique artist-date pairs first
  distinct(influence_creator, influence_release_date) %>%
  # Find first influence date for each artist
  group_by(influence_creator) %>%
  summarize(
    first_influence_date = if(n() > 0) min(influence_release_date) else NA_real_,
    .groups = "drop"
  ) %>%
  # Count new artists by first influence date
  count(first_influence_date, name = "music_count") %>%
  arrange(first_influence_date) %>%
  rename(creator_release_date = first_influence_date) %>%
  # Calculate cumulative unique artists
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_artists_by_date_1,
  x = ~creator_release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Artists",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", creator_release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Annual Count of New Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Influenced Music

# Data Preparation

# Step 1: Count number of influenced music by release date
influence_song_by_date_1 <- mc1_nodes_clean %>%
  filter(name %in% unique(creators_songs_influence_1)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_song_by_date_1,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Music",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Influence",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

6.3.3 Visualizing the career of Chao Wu

# Data Preparation

chosen_creator_2 = "Chao Wu"

# Step 1: Get the node of the chosen creator
chosen_node_2 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_2) %>%
  pull(creator_from) %>%
  unique()

# Step 2: Get the songs that the top creator produced
creator_songs_2 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_2) %>%
  pull(song_to)

# Step 3: Get the songs they have influenced / artists they collaborate with
creators_songs_collaborate_influence_2 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_2,
         infuence_music_collaborate != chosen_node_2) %>%
  pull(infuence_music_collaborate)

# Step 4: Get the songs they have influenced
creators_songs_influence_2 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_2,
         infuence_music_collaborate != chosen_node_2,
         `Edge Colour` == "Influenced By") %>%
  pull(infuence_music_collaborate)

# Step 5: Get the influenced creators of the influenced songs
creators_songs_influence_creators_2 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_2,
         influence_creator != chosen_node_2,
         !is.na(influence_creator),
         infuence_music_collaborate %in% creators_songs_influence_2) %>%
  pull(influence_creator)

all_nodes <- unique(c(chosen_node_2, 
                      creator_songs_2, 
                      creators_songs_collaborate_influence_2,
                      creators_songs_influence_creators_2))

# Create subgraph
sub_graph <- graph %>%
  filter(name %in% all_nodes)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name == chosen_creator_2, 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == chosen_creator_2, chosen_creator_2, NA)
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)

Music Releases

# Data Preparation

# Step 1: Count number of music by release date
music_by_date_2 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_2)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = music_by_date_2,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Notable Music Releases

# Data Preparation

# Step 1: Count number of notable music by release date
notable_music_by_date_2 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_2), notable == TRUE) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = notable_music_by_date_2,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Notable Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Notable Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Artist Influ & Colab

# Data Preparation

# Step 1: Count number of influenced artists by release date
influence_artists_by_date_2 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from %in% unique(chosen_node_2),
         influence_creator != unique(chosen_node_2)) %>%
  # Get unique artist-date pairs first
  distinct(influence_creator, influence_release_date) %>%
  # Find first influence date for each artist
  group_by(influence_creator) %>%
  summarize(
    first_influence_date = if(n() > 0) min(influence_release_date) else NA_real_,
    .groups = "drop"
  ) %>%
  # Count new artists by first influence date
  count(first_influence_date, name = "music_count") %>%
  arrange(first_influence_date) %>%
  rename(creator_release_date = first_influence_date) %>%
  # Calculate cumulative unique artists
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_artists_by_date_2,
  x = ~creator_release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Artists",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", creator_release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Annual Count of New Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Influenced Music

# Data Preparation

# Step 1: Count number of influenced music by release date
influence_song_by_date_2 <- mc1_nodes_clean %>%
  filter(name %in% unique(creators_songs_influence_2)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_song_by_date_2,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Music",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Influence",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

6.3.4 Visualizing the career of Xia Jia

# Data Preparation

chosen_creator_3 = "Xia Jia"

# Step 1: Get the node of the chosen creator
chosen_node_3 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_3) %>%
  pull(creator_from) %>%
  unique()

# Step 2: Get the songs that the top creator produced
creator_songs_3 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_3) %>%
  pull(song_to)

# Step 3: Get the songs they have influenced / artists they collaborate with
creators_songs_collaborate_influence_3 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_3,
         infuence_music_collaborate != chosen_node_3) %>%
  pull(infuence_music_collaborate)

# Step 4: Get the songs they have influenced
creators_songs_influence_3 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_3,
         infuence_music_collaborate != chosen_node_3,
         `Edge Colour` == "Influenced By") %>%
  pull(infuence_music_collaborate)

# Step 5: Get the influenced creators of the influenced songs
creators_songs_influence_creators_3 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_3,
         influence_creator != chosen_node_3,
         !is.na(influence_creator),
         infuence_music_collaborate %in% creators_songs_influence_3) %>%
  pull(influence_creator)

all_nodes <- unique(c(chosen_node_3, 
                      creator_songs_3, 
                      creators_songs_collaborate_influence_3,
                      creators_songs_influence_creators_3))

# Create subgraph
sub_graph <- graph %>%
  filter(name %in% all_nodes)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name == chosen_creator_3, 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == chosen_creator_3, chosen_creator_3, NA)
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)

Music Releases

# Data Preparation

# Step 1: Count number of music by release date
music_by_date_3 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_3)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = music_by_date_3,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Notable Music Releases

# Data Preparation

# Step 1: Count number of notable music by release date
notable_music_by_date_3 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_3), notable == TRUE) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = notable_music_by_date_3,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Notable Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Notable Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Artist Influ & Colab

# Data Preparation

# Step 1: Count number of influenced artists by release date
influence_artists_by_date_3 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from %in% unique(chosen_node_3),
         influence_creator != unique(chosen_node_3)) %>%
  # Get unique artist-date pairs first
  distinct(influence_creator, influence_release_date) %>%
  # Find first influence date for each artist
  group_by(influence_creator) %>%
  summarize(
    first_influence_date = if(n() > 0) min(influence_release_date) else NA_real_,
    .groups = "drop"
  ) %>%
  # Count new artists by first influence date
  count(first_influence_date, name = "music_count") %>%
  arrange(first_influence_date) %>%
  rename(creator_release_date = first_influence_date) %>%
  # Calculate cumulative unique artists
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_artists_by_date_3,
  x = ~creator_release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Artists",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", creator_release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Annual Count of New Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Influenced Music

# Data Preparation

# Step 1: Count number of influenced music by release date
influence_song_by_date_3 <- mc1_nodes_clean %>%
  filter(name %in% unique(creators_songs_influence_3)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_song_by_date_3,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Music",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Influence",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

6.3.5 Visualizing the career of Donna Caldwell

# Data Preparation

chosen_creator_4 = "Donna Caldwell"

# Step 1: Get the node of the chosen creator
chosen_node_4 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_4) %>%
  pull(creator_from) %>%
  unique()

# Step 2: Get the songs that the top creator produced
creator_songs_4 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_4) %>%
  pull(song_to)

# Step 3: Get the songs they have influenced / artists they collaborate with
creators_songs_collaborate_influence_4 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_4,
         infuence_music_collaborate != chosen_node_4) %>%
  pull(infuence_music_collaborate)

# Step 4: Get the songs they have influenced
creators_songs_influence_4 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_4,
         infuence_music_collaborate != chosen_node_4,
         `Edge Colour` == "Influenced By") %>%
  pull(infuence_music_collaborate)

# Step 5: Get the influenced creators of the influenced songs
creators_songs_influence_creators_4 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_4,
         influence_creator != chosen_node_4,
         !is.na(influence_creator),
         infuence_music_collaborate %in% creators_songs_influence_4) %>%
  pull(influence_creator)

all_nodes <- unique(c(chosen_node_4, 
                      creator_songs_4, 
                      creators_songs_collaborate_influence_4,
                      creators_songs_influence_creators_4))

# Create subgraph
sub_graph <- graph %>%
  filter(name %in% all_nodes)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name == chosen_creator_4, 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == chosen_creator_4, chosen_creator_4, NA)
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)

Music Releases

# Data Preparation

# Step 1: Count number of music by release date
music_by_date_4 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_4)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = music_by_date_4,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Notable Music Releases

# Data Preparation

# Step 1: Count number of notable music by release date
notable_music_by_date_4 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_4), notable == TRUE) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = notable_music_by_date_4,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Notable Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Notable Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Artist Influ & Colab

# Data Preparation

# Step 1: Count number of influenced artists by release date
influence_artists_by_date_4 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from %in% unique(chosen_node_4),
         influence_creator != unique(chosen_node_4)) %>%
  # Get unique artist-date pairs first
  distinct(influence_creator, influence_release_date) %>%
  # Find first influence date for each artist
  group_by(influence_creator) %>%
  summarize(
    first_influence_date = if(n() > 0) min(influence_release_date) else NA_real_,
    .groups = "drop"
  ) %>%
  # Count new artists by first influence date
  count(first_influence_date, name = "music_count") %>%
  arrange(first_influence_date) %>%
  rename(creator_release_date = first_influence_date) %>%
  # Calculate cumulative unique artists
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_artists_by_date_4,
  x = ~creator_release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Artists",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", creator_release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Annual Count of New Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Influenced Music

# Data Preparation

# Step 1: Count number of influenced music by release date
influence_song_by_date_4 <- mc1_nodes_clean %>%
  filter(name %in% unique(creators_songs_influence_4)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_song_by_date_4,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Music",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Influence",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

6.3.6 Visualizing the career of Xiulan Ye

# Data Preparation

chosen_creator_5 = "Xiulan Ye"

# Step 1: Get the node of the chosen creator
chosen_node_5 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_5) %>%
  pull(creator_from) %>%
  unique()

# Step 2: Get the songs that the top creator produced
creator_songs_5 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_5) %>%
  pull(song_to)

# Step 3: Get the songs they have influenced / artists they collaborate with
creators_songs_collaborate_influence_5 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_5,
         infuence_music_collaborate != chosen_node_5) %>%
  pull(infuence_music_collaborate)

# Step 4: Get the songs they have influenced
creators_songs_influence_5 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_5,
         infuence_music_collaborate != chosen_node_5,
         `Edge Colour` == "Influenced By") %>%
  pull(infuence_music_collaborate)

# Step 5: Get the influenced creators of the influenced songs
creators_songs_influence_creators_5 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_name == chosen_creator_5,
         influence_creator != chosen_node_5,
         !is.na(influence_creator),
         infuence_music_collaborate %in% creators_songs_influence_5) %>%
  pull(influence_creator)

all_nodes <- unique(c(chosen_node_5, 
                      creator_songs_5, 
                      creators_songs_collaborate_influence_5,
                      creators_songs_influence_creators_5))

# Create subgraph
sub_graph <- graph %>%
  filter(name %in% all_nodes)



# Visualisation

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(node_name == chosen_creator_5, 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(node_name == chosen_creator_5, chosen_creator_5, NA)
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)

Music Releases

# Data Preparation

# Step 1: Count number of music by release date
music_by_date_5 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_5)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = music_by_date_5,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Notable Music Releases

# Data Preparation

# Step 1: Count number of notable music by release date
notable_music_by_date_5 <- mc1_nodes_clean %>%
  filter(name %in% unique(creator_songs_5), notable == TRUE) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = notable_music_by_date_5,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Notable Music Releases",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Released Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Released Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Notable Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Artist Influ & Colab

# Data Preparation

# Step 1: Count number of influenced artists by release date
influence_artists_by_date_5 <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_from %in% unique(chosen_node_5),
         influence_creator != unique(chosen_node_5)) %>%
  # Get unique artist-date pairs first
  distinct(influence_creator, influence_release_date) %>%
  # Find first influence date for each artist
  group_by(influence_creator) %>%
  summarize(
    first_influence_date = if(n() > 0) min(influence_release_date) else NA_real_,
    .groups = "drop"
  ) %>%
  # Count new artists by first influence date
  count(first_influence_date, name = "music_count") %>%
  arrange(first_influence_date) %>%
  rename(creator_release_date = first_influence_date) %>%
  # Calculate cumulative unique artists
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_artists_by_date_5,
  x = ~creator_release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Artists",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", creator_release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Annual Count of New Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

Influenced Music

# Data Preparation

# Step 1: Count number of influenced music by release date
influence_song_by_date_5 <- mc1_nodes_clean %>%
  filter(name %in% unique(creators_songs_influence_5)) %>%
  count(release_date, name = "music_count") %>%
  arrange(release_date) %>%  # Ensure dates are in chronological order
  mutate(cumulative_count = cumsum(music_count))



# Visualisation

plot_ly(
  data = influence_song_by_date_5,
  x = ~release_date,
  y = ~music_count,
  type = "bar",
  name = "Number of Influenced Music",
  marker = list(color = "#2E3192"),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Influence Date: ", release_date,
    "<br>Count: ", music_count
    )
  ) %>%
  add_trace(
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = "Cumulative Count",
    line = list(color = "black", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Influence",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    )
  )

6.3.7 Compare and contrast the five artists’ rise in popularity and influence

The cumulative graphs of the 5 artists (Sailor Shift, Chao Wu, Xia Jia, Donna Caldwell, and Xiulan Ye) will be overlaid for comparison of their musical careers.

Music Releases

# Visualisation

plot_ly(
  data = music_by_date_1,
  x = ~release_date,
  y = ~cumulative_count,
  type = "scatter",
  mode = "lines+markers",
  name = chosen_creator_1,
  line = list(color = "#2E3192", width = 2),
  marker = list(color = "red", size = 6),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Artist: ", chosen_creator_1,
    "<br>Influence Date: ", release_date,
    "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = music_by_date_2,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_2,
    line = list(color = "green", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_2,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = music_by_date_3,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_3,
    line = list(color = "purple", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_3,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
    add_trace(
    data = music_by_date_4,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_4,
    line = list(color = "orange", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_4,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
    add_trace(
    data = music_by_date_5,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_5,
    line = list(color = "skyblue", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_5,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    ),
    annotations = list(
      list(
        x = 2017, 
        y = 2,
        text = "<b>2017:<br>Donna Caldwell <br>& Xiulan Ye</b>",
        xref = "x", yref = "y",
        xanchor = "right",
        showarrow = TRUE, arrowhead = 2,
        ax = -30, ay = -15,
        font = list(color="black", size=12)
      )
    )
  )

Note

Donna Caldwell and Xiulan Ye are at 2017 with a count of 2.

Notable Music Releases

# Visualisation

plot_ly(
  data = notable_music_by_date_1,
  x = ~release_date,
  y = ~cumulative_count,
  type = "scatter",
  mode = "lines+markers",
  name = chosen_creator_1,
  line = list(color = "#2E3192", width = 2),
  marker = list(color = "red", size = 6),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Artist: ", chosen_creator_1,
    "<br>Influence Date: ", release_date,
    "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = notable_music_by_date_2,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_2,
    line = list(color = "green", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_2,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = notable_music_by_date_3,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_3,
    line = list(color = "purple", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_3,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
    add_trace(
    data = notable_music_by_date_4,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_4,
    line = list(color = "orange", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_4,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
    add_trace(
    data = notable_music_by_date_5,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_5,
    line = list(color = "skyblue", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_5,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Notable Music (Song/Album) Releases",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    ),
    annotations = list(
      list(
        x = 2017, 
        y = 1,
        text = "<b>2017:<br>Donna Caldwell <br>& Xiulan Ye</b>",
        xref = "x", yref = "y",
        xanchor = "right",
        showarrow = TRUE, arrowhead = 2,
        ax = -30, ay = -15,
        font = list(color="black", size=12)
      )
    )
  )

Note

Donna Caldwell and Xiulan Ye are at 2017 with a count of 1.

Artist Influ & Colab

# Visualisation

plot_ly(
  data = influence_artists_by_date_1,
  x = ~creator_release_date,
  y = ~cumulative_count,
  type = "scatter",
  mode = "lines+markers",
  name = chosen_creator_1,
  line = list(color = "#2E3192", width = 2),
  marker = list(color = "red", size = 6),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Artist: ", chosen_creator_1,
    "<br>Influence Date: ", creator_release_date,
    "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = influence_artists_by_date_2,
    x = ~creator_release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_2,
    line = list(color = "green", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_2,
      "<br>Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = influence_artists_by_date_3,
    x = ~creator_release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_3,
    line = list(color = "purple", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_3,
      "<br>Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
    add_trace(
    data = influence_artists_by_date_4,
    x = ~creator_release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_4,
    line = list(color = "orange", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_4,
      "<br>Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
    add_trace(
    data = influence_artists_by_date_5,
    x = ~creator_release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_5,
    line = list(color = "skyblue", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_5,
      "<br>Influence Date: ", creator_release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Annual Count of New Artist Influences & Collaborations",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    ),
    annotations = list(
      list(
        x = 2029, 
        y = 159,
        text = "<b>Donna Caldwell</b>",
        xref = "x", yref = "y",
        xanchor = "left",
        showarrow = TRUE, arrowhead = 2,
        ax = 30, ay = 30,
        font = list(color="black", size=12)
      )
    )
  )

Note

Donna Caldwell overlaps with Xiulan Ye

Influenced Music

# Visualisation

plot_ly(
  data = influence_song_by_date_1,
  x = ~release_date,
  y = ~cumulative_count,
  type = "scatter",
  mode = "lines+markers",
  name = chosen_creator_1,
  line = list(color = "#2E3192", width = 2),
  marker = list(color = "red", size = 6),
  hoverinfo = "text",
  hovertext = ~paste0(
    "Artist: ", chosen_creator_1,
    "<br>Influence Date: ", release_date,
    "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = influence_song_by_date_2,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_2,
    line = list(color = "green", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_2,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  add_trace(
    data = influence_song_by_date_3,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_3,
    line = list(color = "purple", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_3,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
    add_trace(
    data = influence_song_by_date_4,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_4,
    line = list(color = "orange", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_4,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
    add_trace(
    data = influence_song_by_date_5,
    x = ~release_date,
    y = ~cumulative_count,
    type = "scatter",
    mode = "lines+markers",
    name = chosen_creator_5,
    line = list(color = "skyblue", width = 2),
    marker = list(color = "red", size = 6),
    hoverinfo = "text",
    hovertext = ~paste0(
      "Artist: ", chosen_creator_5,
      "<br>Influence Date: ", release_date,
      "<br>Cumulative Count: ", cumulative_count
    )
  ) %>%
  layout(
    title = "Yearly Music (Song/Album) Influence",
    margin = list(b = 80, t = 80),      
    xaxis = list(
      title = NA,  
      dtick = 5,
      automargin = TRUE
    ),
    yaxis = list(
      title = "Count",
      automargin = TRUE
    ),
    legend = list(
      orientation = "h",
      x = 0.5,
      xanchor = "center",
      y = -0.1
    ),
    annotations = list(
      list(
        x = 2024, 
        y = 16,
        text = "<b>Donna Caldwell</b>",
        xref = "x", yref = "y",
        xanchor = "right",
        showarrow = TRUE, arrowhead = 2,
        ax = -30, ay = -20,
        font = list(color="black", size=12)
      )
    )
  )

Note

• Donna Caldwell overlaps with Xiulan Ye
• Sailor Shift has no influence

6.3.8 Analysing the relationships between these five artists

# Data Preparation

# Step 1: Find outgoing edges from Chao Wu
out_edges_1 <- mc1_edges_clean %>%
  filter(from == unique(chosen_node_1))

# Step 2: Identify neighbour node names
out_node_names_1 <- out_edges_1$to



# Step 1: Find outgoing edges from Chao Wu
out_edges_2 <- mc1_edges_clean %>%
  filter(from == unique(chosen_node_2))

# Step 2: Identify neighbour node names
out_node_names_2 <- out_edges_2$to



# Step 1: Find outgoing edges from Chao Wu
out_edges_3 <- mc1_edges_clean %>%
  filter(from == unique(chosen_node_3))

# Step 2: Identify neighbour node names
out_node_names_3 <- out_edges_3$to



# Step 1: Find outgoing edges from Chao Wu
out_edges_4 <- mc1_edges_clean %>%
  filter(from == unique(chosen_node_4))

# Step 2: Identify neighbour node names
out_node_names_4 <- out_edges_4$to



# Step 1: Find outgoing edges from Chao Wu
out_edges_5 <- mc1_edges_clean %>%
  filter(from == unique(chosen_node_5))

# Step 2: Identify neighbour node names
out_node_names_5 <- out_edges_5$to



# Build subgraph using names
sub_nodes <- unique(c(chosen_node_1,
                      chosen_node_2,
                      chosen_node_3,
                      chosen_node_4,
                      chosen_node_5,
                      out_node_names_1,
                      out_node_names_2,
                      out_node_names_3,
                      out_node_names_4,
                      out_node_names_5))

sub_graph <- graph %>%
  activate(nodes) %>%
  filter(name %in% sub_nodes)

g <- sub_graph %>%
  ggraph(layout = "fr") + 
  geom_edge_fan(
    aes(
      edge_colour = `Edge Colour`,
      start_cap = circle(1, 'mm'),
      end_cap = circle(1, 'mm')
      ),
    arrow = arrow(length = unit(1, 'mm')),
    alpha = 0.3
  ) +
  geom_point_interactive(
    aes(
      x = x,
      y = y,
      data_id = name,
      colour = `Node Colour`,
      shape = `Node Type`,
      size = ifelse(`node_name` %in% c(chosen_node_1, 
                                       chosen_node_2, 
                                       chosen_node_3, 
                                       chosen_node_4, 
                                       chosen_node_5), 3, 1),
      tooltip = case_when(
        `Node Type` == "Album" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)", node_name, genre, notable, release_date
        ),
        `Node Type` == "Song" ~ sprintf(
          "%s<br/>%s<br/>Notable: %s<br/>(%s)<br/>Single: %s", node_name, genre, notable, release_date, single
        ),
        TRUE ~ sprintf("%s", node_name)
      )
    ),
    show.legend = c(size = FALSE)
  )+ 
  geom_node_text(
    aes(
      label = ifelse(`node_name` == chosen_creator_1, chosen_creator_1,
              ifelse(`node_name` == chosen_creator_2, chosen_creator_2,
              ifelse(`node_name` == chosen_creator_3, chosen_creator_3,
              ifelse(`node_name` == chosen_creator_4, chosen_creator_4,
              ifelse(`node_name` == chosen_creator_5, chosen_creator_5, NA)))))
    ),
    fontface = "bold",
    size = 2.5,
    colour = 'red',
    show.legend = FALSE
  ) +
  scale_shape_manual(
    name = "Node Type",
    values = c(
      "Album" = 16,
      "MusicalGroup" = 15,
      "Person" = 17,
      "Song" = 10
    )
  ) +
  scale_edge_colour_manual(
    name = "Edge Colour",
    values = c(
      `Creator Of` = "#47D45A",
      `Influenced By` = "#FF5757",
      `Member Of` = "#CF57FF"
    )
  ) +
  scale_colour_manual(
    name = "Node Colour",
    values = c(
      "Musician" = "grey50",
      "Oceanus Folk" = "#0027EA",
      "Other Genre" = "#A45200"
    )
  ) +
  theme_graph() +
  theme(legend.text = element_text(size = 6),
        legend.title = element_text(size = 9)) +
  scale_size_identity()

girafe(ggobj = g, width_svg = 7, height_svg = 6)

Tip

• All five artists are solo artists and none of them are part of the same musical group
• Chao Wu, Xia Jia, Donna Caldwell and Xiulan Ye have some collaborative works with each other which can explain why some of their graphs overlaps.
  • Chao Wu, Xia Jia, Donna Caldwell and Xiulan Ye produced Winds of the Basque Shore and Destiny’s Call together.

6.3.8 Predicting who the next three Oceanus Folk stars with be over the next five years

Sailor Shift

• Sailor Shift is ranked 1st in Oceanus Folk star factor.
  • Per the VAST Challenge background, she is already one of music’s biggest superstars and a dominant figure in the Oceanus Folk community.
  • Therefore, she will not be included in these predictions, as she has already achieved superstar status.
• This analysis will continue to identify the next 3 emerging artists poised to become the next Oceanus Folk stars.

Chao Wu

• Chao Wu is the second on the Star Factor list and his influence is large having influenced/collaborated with 190 Artists and influenced the creation of 44 Music with his Oceanus Folk music.
  • Furthermore, he gained his large following despite having only released 4 music.
  • Among his 4 music,3 of them are hits.
  • His last Oceanus Folk work was in 2025 but his work is still being referred to in 2038.

Xia Jia

• Xia Jia is the next on the Star Factor list and her influence is equally large having influenced 178 Artists and 42 Music with her Oceanus Folk music.
  • Furthermore, she has also gained this large following despite having only released 4 music.
  • Similarly, 3 of them are hits.
  • Her last Oceanus Folk work was in 2028 but her works were last referred to in 2030 which is less recent than Chao Wu.

Xiulan Ye

• Both Xiulan Ye and Donna Caldwell have produced the 2 influential songs Basque Shore and Destiny’s Call together.
• Their last Oceanus Folk work was in 2017 and was last referred to in 2030.
• However, Xiulan Ye is slightly more established in the music scene having also produced Unbound from the Doom Metal genre.
• Therefore, the third predicted star will be Xiulan Ye but Donna Caldwell has very good potential as well.

Prediction

In this part of our analysis, we set out to give three predictions of who the next Oceanus Folk stars with be over the next five years.

To do this, we designed a data-driven Star Factor score— a predictive measure using key metrics of an artist’s popularity and influence that capture what it means to be a rising star in the Oceanus Folk music scene:

Total Music Releases: How many works (songs/albums) the artist has produced.

Notable Music Releases: How many of those works are considered hits or critically notable.

Influenced Music Works: The number of downstream works (songs/albums) influenced by this artist’s creations.

Influenced Artists: The number of distinct artists who were directly or indirectly influenced by this artist.

These metrics were computed for every Oceanus Folk artist in the dataset.

library(purrr)

all_oceanus_creators <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  pull(creator_name) %>%
  unique()

compute_creator_metrics <- function(creator_name) {
  creator_node <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
    filter(creator_name == !!creator_name) %>%
    pull(creator_from) %>%
    unique()
  
  creator_songs <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
    filter(creator_name == !!creator_name) %>%
    pull(song_to) %>%
    unique()
  
  notable_count <- mc1_nodes_clean %>%
    filter(name %in% creator_songs, notable == TRUE) %>%
    nrow()
  
  influenced_music <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
    filter(creator_from %in% creator_node, `Edge Colour` == "Influenced By") %>%
    pull(infuence_music_collaborate) %>%
    unique() %>%
    length()
  
  influenced_artists <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
    filter(creator_from %in% creator_node, influence_creator != creator_node) %>%
    pull(influence_creator) %>%
    unique() %>%
    length()
  
  tibble(
    creator_name = creator_name,
    total_music = length(creator_songs),
    notable_music = notable_count,
    influenced_music = influenced_music,
    influenced_artists = influenced_artists
  )
}

Normalize all 4 columns to 0–1 scale

artist_metrics_df <- map_dfr(all_oceanus_creators, compute_creator_metrics)

artist_metrics_df <- artist_metrics_df %>%
  mutate(
    norm_total_music = (total_music - min(total_music)) / (max(total_music) - min(total_music)),
    norm_notable_music = (notable_music - min(notable_music)) / (max(notable_music) - min(notable_music)),
    norm_influenced_music = (influenced_music - min(influenced_music)) / (max(influenced_music) - min(influenced_music)),
    norm_influenced_artists = (influenced_artists - min(influenced_artists)) / (max(influenced_artists) - min(influenced_artists)),
    
    # Star Factor = Equal weighted average of all four metrics
    star_score = 0.25 * norm_total_music +
                 0.25 * norm_notable_music +
                 0.25 * norm_influenced_music +
                 0.25 * norm_influenced_artists
  )

---

Filters the data to include only years 2030–2040.

Groups by artist and year.

For each (artist, year) combination, computes:

total_music: how many distinct songs they created that year. notable_music: how many were marked as notable. influenced_music: how many unique music works they influenced. influenced_artists: how many unique artists they influenced.

# Step 1: Build yearly metrics for Oceanus Folk creators (2030–2040)
artist_yearly_metrics <- oceanus_creator_and_songs_and_influences_and_creators_collaborate %>%
  filter(creator_release_date >= 2030, creator_release_date <= 2040) %>%
  group_by(creator_name, creator_release_date) %>%
  summarise(
    total_music = n_distinct(song_to),
    notable_music = sum(notable == TRUE, na.rm = TRUE),
    influenced_music = n_distinct(infuence_music_collaborate[`Edge Colour` == "Influenced By"], na.rm = TRUE),
    influenced_artists = n_distinct(influence_creator[!is.na(influence_creator)], na.rm = TRUE),
    .groups = "drop"
  )

Compute the trend slope of each metric over time per artist

Fit a linear regression model for each metric across years (y ~ year). Extract the slope (rate of change per year).

For each artist:

It calculates the annual trend (i.e., slope) of each metric from 2030 to 2040. If an artist has no data for some years, NA is filled with 0.

library(purrr)

compute_trend_slope <- function(df, metric) {
  if (nrow(df) < 2) return(NA)  # Not enough years of data
  model <- lm(reformulate("creator_release_date", metric), data = df)
  coef(model)[["creator_release_date"]]
}

# Compute slope of trend over time for each artist
trend_df <- artist_yearly_metrics %>%
  group_by(creator_name) %>%
  summarise(
    trend_music = compute_trend_slope(pick(everything()), "total_music"),
    trend_notable = compute_trend_slope(pick(everything()), "notable_music"),
    trend_influenced_music = compute_trend_slope(pick(everything()), "influenced_music"),
    trend_influenced_artists = compute_trend_slope(pick(everything()), "influenced_artists"),
    .groups = "drop"
  ) %>%
  replace_na(list(
    trend_music = 0,
    trend_notable = 0,
    trend_influenced_music = 0,
    trend_influenced_artists = 0
  ))

Joins the trend slopes to the base dataset artist_metrics_df, which already contains each artist’s cumulative star_score (as of 2040).

Calculates an average trend across the 4 metrics.

Projects the star score 5 years into the future (2045) using “predicted_star_score = star_score (in 2040) + 5 × average trend per year”

# Merge current score (2040) with trend data
predicted_star_df <- artist_metrics_df %>%
  left_join(trend_df, by = "creator_name") %>%
  mutate(
    # Average slope of the 4 metrics
    trend_avg = (trend_music + trend_notable + trend_influenced_music + trend_influenced_artists) / 4,

    # Predict star score in 2045 assuming 5 years of continued trend
    predicted_star_score = star_score + 5 * trend_avg
  ) %>%
  arrange(desc(predicted_star_score))

predicted_star_df %>%
  select(creator_name, star_score, trend_avg, predicted_star_score) %>%
  head(10) %>%
  kable(format = "html", digits = 2, caption = "Top 10 Predicted Oceanus Folk Stars in 2045") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), full_width = FALSE)

Top 10 Predicted Oceanus Folk Stars in 2045

creator_name	star_score	trend_avg	predicted_star_score
Kai Reynolds	0.05	0.75	3.80
Maya Jensen	0.10	0.37	1.97
Sophie Ramirez	0.10	0.30	1.60
Sailor Shift	0.55	0.05	0.81
Tidal Reverie	0.06	0.12	0.68
Coralia Bellweather	0.09	0.00	0.09
Levi Holloway	0.07	0.00	0.07
Jie Li	0.07	0.00	0.07
Marin Thorne	0.07	0.00	0.07
Jonah Calloway	0.07	0.00	0.07